Compression for deep learning in case of sparse values mapped to non-zero value

ABSTRACT

Embodiments described herein provide a processing apparatus comprising compute logic to generate neural network data for a convolutional neural network (CNN) and write the neural network data to a memory buffer. The compute logic additionally includes a direct memory access (DMA) controller including a hardware codec having an encode unit and a decode unit, the DMA controller to read the neural network data from the memory buffer, encode the neural network data via the encode unit, write encoded neural network data to a memory device coupled with the processing apparatus, write metadata for the encoded neural network data to the memory device coupled with the processing apparatus, and decode encoded neural network data via the decode unit in response to a request from the compute logic.

TECHNICAL FIELD

Embodiments generally relate to logic to perform processing operations using general purpose graphics processing units. More particularly, embodiments relate to encoding and decoding feature maps and weights for a convolutional neural network.

BACKGROUND

A neural network can be modeled as collections of neurons that are connected in an acyclic graph. A neural network can receive an input (a single vector), and transform it through a series of hidden layers. Each hidden layer is made up of a set of neurons, where each neuron is fully connected to all neurons in the previous layer, and where neurons in a single layer function completely independently and do not share any connections. The last fully-connected layer is called the “output layer” and in classification settings it represents the class scores. A convolutional neural network (CNN) is similar to a standard neural network. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity. A CNN, however, is explicitly tailored to handle input image data. Based on raw image data input, the network outputs classification score for the input data.

CNN topologies handle a large amount of data. Ideally, this data is processed within on-chip memory. Weight matrix kernel data can generally consume in the order of a few hundred megabytes of memory. Additionally, each layer of the CNN can produce a large amount of data in the form of output feature maps. During operation of a computing system implementing a CNN, kernel data can be read from system main memory. Output feature maps (OFMs) for a CNN layer can also be stored in main memory, where those maps are read as input feature maps (IFMs) for a next layer. Because of the large amount of on-chip data that is processed computing systems executing a CNN, a large amount of power can be expended reading and writing the CNN data.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the present embodiments can be understood in detail, a more particular description of the embodiments, briefly summarized above, may be had by reference to embodiments, some of which are illustrated it the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments and are therefore not to be considered limiting of its scope.

FIG. 1 is a block diagram of a processing system, according to an embodiment;

FIG. 2 is a block diagram of a processor according to an embodiment;

FIG. 3 is a block diagram of a graphics processor, according to an embodiment;

FIG. 4 is a block diagram of a graphics processing engine of a graphics processor in accordance with some embodiments;

FIG. 5 is a block diagram of hardware logic of a graphics processor core, according to some embodiments described herein.

FIGS. 6A-6B illustrate thread execution logic including an array of processing elements employed in a graphics processor core according to embodiments described herein.

FIG. 7 is a block diagram illustrating a graphics processor instruction formats according to some embodiments;

FIG. 8 is a block diagram of a graphics processor according to another embodiment.

FIG. 9A-9B illustrate a graphics processor command format and command sequence, according to some embodiments;

FIG. 10 illustrates exemplary graphics software architecture for a data processing system according to some embodiments;

FIG. 11A is a block diagram illustrating an IP core development system, according to an embodiment;

FIG. 11B illustrates a cross-section side view of an integrated circuit package assembly, according to some embodiments described herein;

FIG. 12 is a block diagram illustrating an exemplary system on a chip integrated circuit, according to an embodiment;

FIGS. 13A-13B are block diagrams illustrating exemplary graphics processors for use within an SoC, according to embodiments described herein;

FIGS. 14A-14B illustrate additional exemplary graphics processor logic according to embodiments described herein;

FIG. 15 is an illustration of primitive elements of a convolutional neural network;

FIG. 16A-16B are illustrations of a conventional implementation of a convolutional neural network;

FIGS. 17A-17B illustrate memory layouts for feature maps and kernel data, according to an embodiment;

FIG. 18 illustrates an example of a memory layout with raw data relative to the proposed compressed memory layout for the kernel block units, according to an embodiment;

FIG. 19 illustrates a compute architecture configured to enable compressed transmission of CNN data, according to an embodiment;

FIG. 20 illustrates a bit stream header, according to an embodiment;

FIG. 21 illustrates a table encoding bit stream layout, according to an embodiment;

FIG. 22 illustrates various layouts for bit stream encoding, according to embodiments;

FIGS. 23-26 illustrates decode of exemplary bit streams;

FIG. 27 is a flow diagram illustrating a method of performing CNN operations, according to an embodiment;

FIG. 28 is a flow diagram of a method of encoding CNN data, according to an embodiment;

FIG. 29 illustrates compression using the significance map mode of compression, according to an additional embodiment;

FIG. 30 illustrates compression using the unique value coordinate mode of compression, according to an additional embodiment;

FIG. 31 illustrates encoding and decoding operations with separate metadata, according to an embodiment;

FIG. 32 illustrates an exemplary codec system, according to an embodiment;

FIG. 33 illustrates compression for machine learning data having a single high frequency value, according to embodiments described herein;

FIG. 34 is a flow diagram illustrating compression for deep learning where sparse values mapped to non-zero value, according to an embodiment; and

FIG. 35 is a block diagram of a computing device, according to an embodiment.

DESCRIPTION OF EMBODIMENTS

Embodiments described herein provide a CNN processing system including automatic and lossless data compression for feature map memory operations. A CNN optimized system on a chip integrated circuit is provided in which feature map writes to memory are automatically and losslessly compressed, while feature map reads are automatically decompressed. This automatic compression and decompression preserves memory bus bandwidth and reduces system memory access power requirements when performing CNN operations. Additional embodiments described herein extend neural network compression techniques to support additional compression mechanism to map high frequency values to zeros. This additional compression mechanism enables hardware support for low precision matrix multiplication libraries that map zero values to non-zero values. These techniques can be implemented within a hardware module that enables automatic, runtime compression of machine learning data.

For the purposes of explanation, numerous specific details are set forth to provide a thorough understanding of the various embodiments described below. However, it will be apparent to a skilled practitioner in the art that the embodiments may be practiced without some of these specific details. In other instances, well-known structures and devices are shown in block diagram form to avoid obscuring the underlying principles, and to provide a more thorough understanding of embodiments. Although some of the following embodiments are described with reference to a graphics processor, the techniques and teachings described herein may be applied to various types of circuits or semiconductor devices, including general purpose processing devices or graphic processing devices. Reference herein to “one embodiment” or “an embodiment” indicate that a particular feature, structure, or characteristic described in connection or association with the embodiment can be included in at least one of such embodiments. However, the appearances of the phrase “in one embodiment” in various places in the specification do not necessarily all refer to the same embodiment.

In the following description and claims, the terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms are not intended as synonyms for each other. “Coupled” is used to indicate that two or more elements, which may or may not be in direct physical or electrical contact with each other, co-operate or interact with each other. “Connected” is used to indicate the establishment of communication between two or more elements that are coupled with each other.

In the description that follows, FIGS. 1-14 provide an overview of exemplary data processing system and graphics processor logic that incorporates or relates to the various embodiments. FIGS. 15-35 provide specific details of the various embodiments. Some aspects of the following embodiments are described with reference to a graphics processor, while other aspects are described with respect to a general-purpose processor, such as a central processing unit (CPU). Similar techniques and teachings can be applied to other types of circuits or semiconductor devices, including but not limited to a many integrated core processor, a GPU cluster, or one or more instances of a field programmable gate array (FPGA). In general, the teachings are applicable to any processor or machine that manipulates or processes image (e.g., sample, pixel), vertex data, or geometry data.

System Overview

FIG. 1 is a block diagram of a processing system 100, according to an embodiment. In various embodiments the system 100 includes one or more processors 102 and one or more graphics processors 108, and may be a single processor desktop system, a multiprocessor workstation system, or a server system having a large number of processors 102 or processor cores 107. In one embodiment, the system 100 is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices.

In one embodiment the system 100 can include, or be incorporated within a server-based gaming platform, a game console, including a game and media console, a mobile gaming console, a handheld game console, or an online game console. In some embodiments the system 100 is a mobile phone, smart phone, tablet computing device or mobile Internet device. The processing system 100 can also include, couple with, or be integrated within a wearable device, such as a smart watch wearable device, smart eyewear device, augmented reality device, or virtual reality device. In some embodiments, the processing system 100 is a television or set top box device having one or more processors 102 and a graphical interface generated by one or more graphics processors 108.

In some embodiments, the one or more processors 102 each include one or more processor cores 107 to process instructions which, when executed, perform operations for system and user software. In some embodiments, each of the one or more processor cores 107 is configured to process a specific instruction set 109. In some embodiments, instruction set 109 may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). Multiple processor cores 107 may each process a different instruction set 109, which may include instructions to facilitate the emulation of other instruction sets. Processor core 107 may also include other processing devices, such a Digital Signal Processor (DSP).

In some embodiments, the processor 102 includes cache memory 104. Depending on the architecture, the processor 102 can have a single internal cache or multiple levels of internal cache. In some embodiments, the cache memory is shared among various components of the processor 102. In some embodiments, the processor 102 also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores 107 using known cache coherency techniques. A register file 106 is additionally included in processor 102 which may include different types of registers for storing different types of data (e.g., integer registers, floating-point registers, status registers, and an instruction pointer register). Some registers may be general-purpose registers, while other registers may be specific to the design of the processor 102.

In some embodiments, one or more processor(s) 102 are coupled with one or more interface bus(es) 110 to transmit communication signals such as address, data, or control signals between processor 102 and other components in the system 100. The interface bus 110, in one embodiment, can be a processor bus, such as a version of the Direct Media Interface (DMI) bus. However, processor busses are not limited to the DMI bus, and may include one or more Peripheral Component Interconnect buses (e.g., PCI, PCI Express), memory busses, or other types of interface busses. In one embodiment the processor(s) 102 include an integrated memory controller 116 and a platform controller hub 130. The memory controller 116 facilitates communication between a memory device and other components of the system 100, while the platform controller hub (PCH) 130 provides connections to I/O devices via a local I/O bus.

The memory device 120 can be a dynamic random-access memory (DRAM) device, a static random access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In one embodiment the memory device 120 can operate as system memory for the system 100, to store data 122 and instructions 121 for use when the one or more processors 102 executes an application or process. Memory controller 116 also couples with an optional external graphics processor 112, which may communicate with the one or more graphics processors 108 in processors 102 to perform graphics and media operations. In some embodiments a display device 111 can connect to the processor(s) 102. The display device 111 can be one or more of an internal display device, as in a mobile electronic device or a laptop device or an external display device attached via a display interface (e.g., DisplayPort, etc.). In one embodiment the display device 111 can be a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications.

In some embodiments the platform controller hub 130 enables peripherals to connect to memory device 120 and processor 102 via a high-speed I/O bus. The I/O peripherals include, but are not limited to, an audio controller 146, a network controller 134, a firmware interface 128, a wireless transceiver 126, touch sensors 125, a data storage device 124 (e.g., hard disk drive, flash memory, etc.). The data storage device 124 can connect via a storage interface (e.g., SATA) or via a peripheral bus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCI Express). The touch sensors 125 can include touch screen sensors, pressure sensors, or fingerprint sensors. The wireless transceiver 126 can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver such as a 3G, 4G, or Long-Term Evolution (LTE) transceiver. The firmware interface 128 enables communication with system firmware, and can be, for example, a unified extensible firmware interface (UEFI). The network controller 134 can enable a network connection to a wired network. In some embodiments, a high-performance network controller (not shown) couples with the interface bus 110. The audio controller 146, in one embodiment, is a multi-channel high definition audio controller. In one embodiment the system 100 includes an optional legacy I/O controller 140 for coupling legacy (e.g., Personal System 2 (PS/2)) devices to the system. The platform controller hub 130 can also connect to one or more Universal Serial Bus (USB) controllers 142 connect input devices, such as keyboard and mouse 143 combinations, a camera 144, or other USB input devices.

It will be appreciated that the system 100 shown is exemplary and not limiting, as other types of data processing systems that are differently configured may also be used. For example, an instance of the memory controller 116 and platform controller hub 130 may be integrated into a discreet external graphics processor, such as the external graphics processor 112. In one embodiment the platform controller hub 130 and/or memory controller 160 may be external to the one or more processor(s) 102. For example, the system 100 can include an external memory controller 116 and platform controller hub 130, which may be configured as a memory controller hub and peripheral controller hub within a system chipset that is in communication with the processor(s) 102.

FIG. 2 is a block diagram of an embodiment of a processor 200 having one or more processor cores 202A-202N, an integrated memory controller 214, and an integrated graphics processor 208. Those elements of FIG. 2 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. Processor 200 can include additional cores up to and including additional core 202N represented by the dashed lined boxes. Each of processor cores 202A-202N includes one or more internal cache units 204A-204N. In some embodiments each processor core also has access to one or more shared cached units 206.

The internal cache units 204A-204N and shared cache units 206 represent a cache memory hierarchy within the processor 200. The cache memory hierarchy may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where the highest level of cache before external memory is classified as the LLC. In some embodiments, cache coherency logic maintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or more bus controller units 216 and a system agent core 210. The one or more bus controller units 216 manage a set of peripheral buses, such as one or more PCI or PCI express busses. System agent core 210 provides management functionality for the various processor components. In some embodiments, system agent core 210 includes one or more integrated memory controllers 214 to manage access to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 202A-202N include support for simultaneous multi-threading. In such embodiment, the system agent core 210 includes components for coordinating and operating cores 202A-202N during multi-threaded processing. System agent core 210 may additionally include a power control unit (PCU), which includes logic and components to regulate the power state of processor cores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphics processor 208 to execute graphics processing operations. In some embodiments, the graphics processor 208 couples with the set of shared cache units 206, and the system agent core 210, including the one or more integrated memory controllers 214. In some embodiments, the system agent core 210 also includes a display controller 211 to drive graphics processor output to one or more coupled displays. In some embodiments, display controller 211 may also be a separate module coupled with the graphics processor via at least one interconnect, or may be integrated within the graphics processor 208.

In some embodiments, a ring based interconnect unit 212 is used to couple the internal components of the processor 200. However, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques, including techniques well known in the art. In some embodiments, graphics processor 208 couples with the ring interconnect 212 via an I/O link 213.

The exemplary I/O link 213 represents at least one of multiple varieties of I/O interconnects, including an on package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module 218, such as an eDRAM module. In some embodiments, each of the processor cores 202A-202N and graphics processor 208 use embedded memory modules 218 as a shared Last Level Cache.

In some embodiments, processor cores 202A-202N are homogenous cores executing the same instruction set architecture. In another embodiment, processor cores 202A-202N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores 202A-202N execute a first instruction set, while at least one of the other cores executes a subset of the first instruction set or a different instruction set. In one embodiment processor cores 202A-202N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. Additionally, processor 200 can be implemented on one or more chips or as an SoC integrated circuit having the illustrated components, in addition to other components.

FIG. 3 is a block diagram of a graphics processor 300, which may be a discrete graphics processing unit, or may be a graphics processor integrated with a plurality of processing cores. In some embodiments, the graphics processor communicates via a memory mapped I/O interface to registers on the graphics processor and with commands placed into the processor memory. In some embodiments, graphics processor 300 includes a memory interface 314 to access memory. Memory interface 314 can be an interface to local memory, one or more internal caches, one or more shared external caches, and/or to system memory.

In some embodiments, graphics processor 300 also includes a display controller 302 to drive display output data to a display device 320. Display controller 302 includes hardware for one or more overlay planes for the display and composition of multiple layers of video or user interface elements. The display device 320 can be an internal or external display device. In one embodiment the display device 320 is a head mounted display device, such as a virtual reality (VR) display device or an augmented reality (AR) display device. In some embodiments, graphics processor 300 includes a video codec engine 306 to encode, decode, or transcode media to, from, or between one or more media encoding formats, including, but not limited to Moving Picture Experts Group (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, as well as the Society of Motion Picture & Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block image transfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizer operations including, for example, bit-boundary block transfers. However, in one embodiment, 2D graphics operations are performed using one or more components of graphics processing engine (GPE) 310. In some embodiments, GPE 310 is a compute engine for performing graphics operations, including three-dimensional (3D) graphics operations and media operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing 3D operations, such as rendering three-dimensional images and scenes using processing functions that act upon 3D primitive shapes (e.g., rectangle, triangle, etc.). The 3D pipeline 312 includes programmable and fixed function elements that perform various tasks within the element and/or spawn execution threads to a 3D/Media sub-system 315. While 3D pipeline 312 can be used to perform media operations, an embodiment of GPE 310 also includes a media pipeline 316 that is specifically used to perform media operations, such as video post-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function or programmable logic units to perform one or more specialized media operations, such as video decode acceleration, video de-interlacing, and video encode acceleration in place of, or on behalf of video codec engine 306. In some embodiments, media pipeline 316 additionally includes a thread spawning unit to spawn threads for execution on 3D/Media sub-system 315. The spawned threads perform computations for the media operations on one or more graphics execution units included in 3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executing threads spawned by 3D pipeline 312 and media pipeline 316. In one embodiment, the pipelines send thread execution requests to 3D/Media subsystem 315, which includes thread dispatch logic for arbitrating and dispatching the various requests to available thread execution resources. The execution resources include an array of graphics execution units to process the 3D and media threads. In some embodiments, 3D/Media subsystem 315 includes one or more internal caches for thread instructions and data. In some embodiments, the subsystem also includes shared memory, including registers and addressable memory, to share data between threads and to store output data.

Graphics Processing Engine

FIG. 4 is a block diagram of a graphics processing engine 410 of a graphics processor in accordance with some embodiments. In one embodiment, the graphics processing engine (GPE) 410 is a version of the GPE 310 shown in FIG. 3. Elements of FIG. 4 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. For example, the 3D pipeline 312 and media pipeline 316 of FIG. 3 are illustrated. The media pipeline 316 is optional in some embodiments of the GPE 410 and may not be explicitly included within the GPE 410. For example and in at least one embodiment, a separate media and/or image processor is coupled to the GPE 410.

In some embodiments, GPE 410 couples with or includes a command streamer 403, which provides a command stream to the 3D pipeline 312 and/or media pipelines 316. In some embodiments, command streamer 403 is coupled with memory, which can be system memory, or one or more of internal cache memory and shared cache memory. In some embodiments, command streamer 403 receives commands from the memory and sends the commands to 3D pipeline 312 and/or media pipeline 316. The commands are directives fetched from a ring buffer, which stores commands for the 3D pipeline 312 and media pipeline 316. In one embodiment, the ring buffer can additionally include batch command buffers storing batches of multiple commands. The commands for the 3D pipeline 312 can also include references to data stored in memory, such as but not limited to vertex and geometry data for the 3D pipeline 312 and/or image data and memory objects for the media pipeline 316. The 3D pipeline 312 and media pipeline 316 process the commands and data by performing operations via logic within the respective pipelines or by dispatching one or more execution threads to a graphics core array 414. In one embodiment the graphics core array 414 include one or more blocks of graphics cores (e.g., graphics core(s) 415A, graphics core(s) 415B), each block including one or more graphics cores. Each graphics core includes a set of graphics execution resources that includes general purpose and graphics specific execution logic to perform graphics and compute operations, as well as fixed function texture processing and/or machine learning and artificial intelligence acceleration logic.

In various embodiments the 3D pipeline 312 includes fixed function and programmable logic to process one or more shader programs, such as vertex shaders, geometry shaders, pixel shaders, fragment shaders, compute shaders, or other shader programs, by processing the instructions and dispatching execution threads to the graphics core array 414. The graphics core array 414 provides a unified block of execution resources for use in processing these shader programs. Multi-purpose execution logic (e.g., execution units) within the graphics core(s) 415A-414B of the graphic core array 414 includes support for various 3D API shader languages and can execute multiple simultaneous execution threads associated with multiple shaders.

In some embodiments the graphics core array 414 also includes execution logic to perform media functions, such as video and/or image processing. In one embodiment, the execution units additionally include general-purpose logic that is programmable to perform parallel general purpose computational operations, in addition to graphics processing operations. The general purpose logic can perform processing operations in parallel or in conjunction with general purpose logic within the processor core(s) 107 of FIG. 1 or core 202A-202N as in FIG. 2.

Output data generated by threads executing on the graphics core array 414 can output data to memory in a unified return buffer (URB) 418. The URB 418 can store data for multiple threads. In some embodiments the URB 418 may be used to send data between different threads executing on the graphics core array 414. In some embodiments the URB 418 may additionally be used for synchronization between threads on the graphics core array and fixed function logic within the shared function logic 420.

In some embodiments, graphics core array 414 is scalable, such that the array includes a variable number of graphics cores, each having a variable number of execution units based on the target power and performance level of GPE 410. In one embodiment the execution resources are dynamically scalable, such that execution resources may be enabled or disabled as needed.

The graphics core array 414 couples with shared function logic 420 that includes multiple resources that are shared between the graphics cores in the graphics core array. The shared functions within the shared function logic 420 are hardware logic units that provide specialized supplemental functionality to the graphics core array 414. In various embodiments, shared function logic 420 includes but is not limited to sampler 421, math 422, and inter-thread communication (ITC) 423 logic. Additionally, some embodiments implement one or more cache(s) 425 within the shared function logic 420.

A shared function is implemented where the demand for a given specialized function is insufficient for inclusion within the graphics core array 414. Instead a single instantiation of that specialized function is implemented as a stand-alone entity in the shared function logic 420 and shared among the execution resources within the graphics core array 414. The precise set of functions that are shared between the graphics core array 414 and included within the graphics core array 414 varies across embodiments. In some embodiments, specific shared functions within the shared function logic 420 that are used extensively by the graphics core array 414 may be included within shared function logic 416 within the graphics core array 414. In various embodiments, the shared function logic 416 within the graphics core array 414 can include some or all logic within the shared function logic 420. In one embodiment, all logic elements within the shared function logic 420 may be duplicated within the shared function logic 416 of the graphics core array 414. In one embodiment the shared function logic 420 is excluded in favor of the shared function logic 416 within the graphics core array 414.

FIG. 5 is a block diagram of hardware logic of a graphics processor core 500, according to some embodiments described herein. Elements of FIG. 5 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. The illustrated graphics processor core 500, in some embodiments, is included within the graphics core array 414 of FIG. 4. The graphics processor core 500, sometimes referred to as a core slice, can be one or multiple graphics cores within a modular graphics processor. The graphics processor core 500 is exemplary of one graphics core slice, and a graphics processor as described herein may include multiple graphics core slices based on target power and performance envelopes. Each graphics core 500 can include a fixed function block 530 coupled with multiple sub-cores 501A-501F, also referred to as sub-slices, that include modular blocks of general purpose and fixed function logic.

In some embodiments the fixed function block 530 includes a geometry/fixed function pipeline 536 that can be shared by all sub-cores in the graphics processor 500, for example, in lower performance and/or lower power graphics processor implementations. In various embodiments, the geometry/fixed function pipeline 536 includes a 3D fixed function pipeline (e.g., 3D pipeline 312 as in FIG. 3 and FIG. 4) a video front-end unit, a thread spawner and thread dispatcher, and a unified return buffer manager, which manages unified return buffers, such as the unified return buffer 418 of FIG. 4.

In one embodiment the fixed function block 530 also includes a graphics SoC interface 537, a graphics microcontroller 538, and a media pipeline 539. The graphics SoC interface 537 provides an interface between the graphics core 500 and other processor cores within a system on a chip integrated circuit. The graphics microcontroller 538 is a programmable sub-processor that is configurable to manage various functions of the graphics processor 500, including thread dispatch, scheduling, and pre-emption. The media pipeline 539 (e.g., media pipeline 316 of FIG. 3 and FIG. 4) includes logic to facilitate the decoding, encoding, pre-processing, and/or post-processing of multimedia data, including image and video data. The media pipeline 539 implement media operations via requests to compute or sampling logic within the sub-cores 501-501F.

In one embodiment the SoC interface 537 enables the graphics core 500 to communicate with general purpose application processor cores (e.g., CPUs) and/or other components within an SoC, including memory hierarchy elements such as a shared last level cache memory, the system RAM, and/or embedded on-chip or on-package DRAM. The SoC interface 537 can also enable communication with fixed function devices within the SoC, such as camera imaging pipelines, and enables the use of and/or implements global memory atomics that may be shared between the graphics core 500 and CPUs within the SoC. The SoC interface 537 can also implement power management controls for the graphics core 500 and enable an interface between a clock domain of the graphic core 500 and other clock domains within the SoC. In one embodiment the SoC interface 537 enables receipt of command buffers from a command streamer and global thread dispatcher that are configured to provide commands and instructions to each of one or more graphics cores within a graphics processor. The commands and instructions can be dispatched to the media pipeline 539, when media operations are to be performed, or a geometry and fixed function pipeline (e.g., geometry and fixed function pipeline 536, geometry and fixed function pipeline 514) when graphics processing operations are to be performed.

The graphics microcontroller 538 can be configured to perform various scheduling and management tasks for the graphics core 500. In one embodiment the graphics microcontroller 538 can perform graphics and/or compute workload scheduling on the various graphics parallel engines within execution unit (EU) arrays 502A-502F, 504A-504F within the sub-cores 501A-501F. In this scheduling model, host software executing on a CPU core of an SoC including the graphics core 500 can submit workloads one of multiple graphic processor doorbells, which invokes a scheduling operation on the appropriate graphics engine. Scheduling operations include determining which workload to run next, submitting a workload to a command streamer, pre-empting existing workloads running on an engine, monitoring progress of a workload, and notifying host software when a workload is complete. In one embodiment the graphics microcontroller 538 can also facilitate low-power or idle states for the graphics core 500, providing the graphics core 500 with the ability to save and restore registers within the graphics core 500 across low-power state transitions independently from the operating system and/or graphics driver software on the system.

The graphics core 500 may have greater than or fewer than the illustrated sub-cores 501A-501F, up to N modular sub-cores. For each set of N sub-cores, the graphics core 500 can also include shared function logic 510, shared and/or cache memory 512, a geometry/fixed function pipeline 514, as well as additional fixed function logic 516 to accelerate various graphics and compute processing operations. The shared function logic 510 can include logic units associated with the shared function logic 420 of FIG. 4 (e.g., sampler, math, and/or inter-thread communication logic) that can be shared by each N sub-cores within the graphics core 500. The shared and/or cache memory 512 can be a last-level cache for the set of N sub-cores 501A-501F within the graphics core 500, and can also serve as shared memory that is accessible by multiple sub-cores. The geometry/fixed function pipeline 514 can be included instead of the geometry/fixed function pipeline 536 within the fixed function block 530 and can include the same or similar logic units.

In one embodiment the graphics core 500 includes additional fixed function logic 516 that can include various fixed function acceleration logic for use by the graphics core 500. In one embodiment the additional fixed function logic 516 includes an additional geometry pipeline for use in position only shading. In position-only shading, two geometry pipelines exist, the full geometry pipeline within the geometry/fixed function pipeline 516, 536, and a cull pipeline, which is an additional geometry pipeline which may be included within the additional fixed function logic 516. In one embodiment the cull pipeline is a trimmed down version of the full geometry pipeline. The full pipeline and the cull pipeline can execute different instances of the same application, each instance having a separate context. Position only shading can hide long cull runs of discarded triangles, enabling shading to be completed earlier in some instances. For example and in one embodiment the cull pipeline logic within the additional fixed function logic 516 can execute position shaders in parallel with the main application and generally generates critical results faster than the full pipeline, as the cull pipeline fetches and shades only the position attribute of the vertices, without performing rasterization and rendering of the pixels to the frame buffer. The cull pipeline can use the generated critical results to compute visibility information for all the triangles without regard to whether those triangles are culled. The full pipeline (which in this instance may be referred to as a replay pipeline) can consume the visibility information to skip the culled triangles to shade only the visible triangles that are finally passed to the rasterization phase.

In one embodiment the additional fixed function logic 516 can also include machine-learning acceleration logic, such as fixed function matrix multiplication logic, for implementations including optimizations for machine learning training or inferencing.

Within each graphics sub-core 501A-501F includes a set of execution resources that may be used to perform graphics, media, and compute operations in response to requests by graphics pipeline, media pipeline, or shader programs. The graphics sub-cores 501A-501F include multiple EU arrays 502A-502F, 504A-504F, thread dispatch and inter-thread communication (TD/IC) logic 503A-503F, a 3D (e.g., texture) sampler 505A-505F, a media sampler 506A-506F, a shader processor 507A-507F, and shared local memory (SLM) 508A-508F. The EU arrays 502A-502F, 504A-504F each include multiple execution units, which are general-purpose graphics processing units capable of performing floating-point and integer/fixed-point logic operations in service of a graphics, media, or compute operation, including graphics, media, or compute shader programs. The TD/IC logic 503A-503F performs local thread dispatch and thread control operations for the execution units within a sub-core and facilitate communication between threads executing on the execution units of the sub-core. The 3D sampler 505A-505F can read texture or other 3D graphics related data into memory. The 3D sampler can read texture data differently based on a configured sample state and the texture format associated with a given texture. The media sampler 506A-506F can perform similar read operations based on the type and format associated with media data. In one embodiment, each graphics sub-core 501A-501F can alternately include a unified 3D and media sampler. Threads executing on the execution units within each of the sub-cores 501A-501F can make use of shared local memory 508A-508F within each sub-core, to enable threads executing within a thread group to execute using a common pool of on-chip memory.

Execution Units

FIGS. 6A-6B illustrate thread execution logic 600 including an array of processing elements employed in a graphics processor core according to embodiments described herein. Elements of FIGS. 6A-6B having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. FIG. 6A illustrates an overview of thread execution logic 600, which can include a variant of the hardware logic illustrated with each sub-core 501A-501F of FIG. 5. FIG. 6B illustrates exemplary internal details of an execution unit.

As illustrated in FIG. 6A, in some embodiments thread execution logic 600 includes a shader processor 602, a thread dispatcher 604, instruction cache 606, a scalable execution unit array including a plurality of execution units 608A-608N, a sampler 610, a data cache 612, and a data port 614. In one embodiment the scalable execution unit array can dynamically scale by enabling or disabling one or more execution units (e.g., any of execution unit 608A, 608B, 608C, 608D, through 608N-1 and 608N) based on the computational requirements of a workload. In one embodiment the included components are interconnected via an interconnect fabric that links to each of the components. In some embodiments, thread execution logic 600 includes one or more connections to memory, such as system memory or cache memory, through one or more of instruction cache 606, data port 614, sampler 610, and execution units 608A-608N. In some embodiments, each execution unit (e.g. 608A) is a stand-alone programmable general purpose computational unit that is capable of executing multiple simultaneous hardware threads while processing multiple data elements in parallel for each thread. In various embodiments, the array of execution units 608A-608N is scalable to include any number individual execution units.

In some embodiments, the execution units 608A-608N are primarily used to execute shader programs. A shader processor 602 can process the various shader programs and dispatch execution threads associated with the shader programs via a thread dispatcher 604. In one embodiment the thread dispatcher includes logic to arbitrate thread initiation requests from the graphics and media pipelines and instantiate the requested threads on one or more execution unit in the execution units 608A-608N. For example, a geometry pipeline can dispatch vertex, tessellation, or geometry shaders to the thread execution logic for processing. In some embodiments, thread dispatcher 604 can also process runtime thread spawning requests from the executing shader programs.

In some embodiments, the execution units 608A-608N support an instruction set that includes native support for many standard 3D graphics shader instructions, such that shader programs from graphics libraries (e.g., Direct 3D and OpenGL) are executed with a minimal translation. The execution units support vertex and geometry processing (e.g., vertex programs, geometry programs, vertex shaders), pixel processing (e.g., pixel shaders, fragment shaders) and general-purpose processing (e.g., compute and media shaders). Each of the execution units 608A-608N is capable of multi-issue single instruction multiple data (SIMD) execution and multi-threaded operation enables an efficient execution environment in the face of higher latency memory accesses. Each hardware thread within each execution unit has a dedicated high-bandwidth register file and associated independent thread-state. Execution is multi-issue per clock to pipelines capable of integer, single and double precision floating-point operations, SIMD branch capability, logical operations, transcendental operations, and other miscellaneous operations. While waiting for data from memory or one of the shared functions, dependency logic within the execution units 608A-608N causes a waiting thread to sleep until the requested data has been returned. While the waiting thread is sleeping, hardware resources may be devoted to processing other threads. For example, during a delay associated with a vertex shader operation, an execution unit can perform operations for a pixel shader, fragment shader, or another type of shader program, including a different vertex shader.

Each execution unit in execution units 608A-608N operates on arrays of data elements. The number of data elements is the “execution size,” or the number of channels for the instruction. An execution channel is a logical unit of execution for data element access, masking, and flow control within instructions. The number of channels may be independent of the number of physical Arithmetic Logic Units (ALUs) or Floating-point Units (FPUs) for a particular graphics processor. In some embodiments, execution units 608A-608N support integer and floating-point data types.

The execution unit instruction set includes SIMD instructions. The various data elements can be stored as a packed data type in a register and the execution unit will process the various elements based on the data size of the elements. For example, when operating on a 256-bit wide vector, the 256 bits of the vector are stored in a register and the execution unit operates on the vector as four separate 64-bit packed data elements (Quad-Word (QW) size data elements), eight separate 32-bit packed data elements (Double Word (DW) size data elements), sixteen separate 16-bit packed data elements (Word (W) size data elements), or thirty-two separate 8-bit data elements (byte (B) size data elements). However, different vector widths and register sizes are possible.

In one embodiment one or more execution units can be combined into a fused execution unit 609A-609N having thread control logic (607A-607N) that is common to the fused EUs. Multiple EUs can be fused into an EU group. Each EU in the fused EU group can be configured to execute a separate SIMD hardware thread. The number of EUs in a fused EU group can vary according to embodiments. Additionally, various SIMD widths can be performed per-EU, including but not limited to SIMD8, SIMD16, and SIMD32. Each fused graphics execution unit 609A-609N includes at least two execution units. For example, fused execution unit 609A includes a first EU 608A, second EU 608B, and thread control logic 607A that is common to the first EU 608A and the second EU 608B. The thread control logic 607A controls threads executed on the fused graphics execution unit 609A, allowing each EU within the fused execution units 609A-609N to execute using a common instruction pointer register.

One or more internal instruction caches (e.g., 606) are included in the thread execution logic 600 to cache thread instructions for the execution units. In some embodiments, one or more data caches (e.g., 612) are included to cache thread data during thread execution. In some embodiments, a sampler 610 is included to provide texture sampling for 3D operations and media sampling for media operations. In some embodiments, sampler 610 includes specialized texture or media sampling functionality to process texture or media data during the sampling process before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send thread initiation requests to thread execution logic 600 via thread spawning and dispatch logic. Once a group of geometric objects has been processed and rasterized into pixel data, pixel processor logic (e.g., pixel shader logic, fragment shader logic, etc.) within the shader processor 602 is invoked to further compute output information and cause results to be written to output surfaces (e.g., color buffers, depth buffers, stencil buffers, etc.). In some embodiments, a pixel shader or fragment shader calculates the values of the various vertex attributes that are to be interpolated across the rasterized object. In some embodiments, pixel processor logic within the shader processor 602 then executes an application programming interface (API)-supplied pixel or fragment shader program. To execute the shader program, the shader processor 602 dispatches threads to an execution unit (e.g., 608A) via thread dispatcher 604. In some embodiments, shader processor 602 uses texture sampling logic in the sampler 610 to access texture data in texture maps stored in memory. Arithmetic operations on the texture data and the input geometry data compute pixel color data for each geometric fragment, or discards one or more pixels from further processing.

In some embodiments, the data port 614 provides a memory access mechanism for the thread execution logic 600 to output processed data to memory for further processing on a graphics processor output pipeline. In some embodiments, the data port 614 includes or couples to one or more cache memories (e.g., data cache 612) to cache data for memory access via the data port.

As illustrated in FIG. 6B, a graphics execution unit 608 can include an instruction fetch unit 637, a general register file array (GRF) 624, an architectural register file array (ARF) 626, a thread arbiter 622, a send unit 630, a branch unit 632, a set of SIMD floating-point units (FPUs) 634, and in one embodiment a set of dedicated integer SIMD ALUs 635. The GRF 624 and ARF 626 includes the set of general register files and architecture register files associated with each simultaneous hardware thread that may be active in the graphics execution unit 608. In one embodiment, per thread architectural state is maintained in the ARF 626, while data used during thread execution is stored in the GRF 624. The execution state of each thread, including the instruction pointers for each thread, can be held in thread-specific registers in the ARF 626.

In one embodiment the graphics execution unit 608 has an architecture that is a combination of Simultaneous Multi-Threading (SMT) and fine-grained Interleaved Multi-Threading (IMT). The architecture has a modular configuration that can be fine tuned at design time based on a target number of simultaneous threads and number of registers per execution unit, where execution unit resources are divided across logic used to execute multiple simultaneous threads.

In one embodiment, the graphics execution unit 608 can co-issue multiple instructions, which may each be different instructions. The thread arbiter 622 of the graphics execution unit thread 608 can dispatch the instructions to one of the send unit 630, branch unit 642, or SIMD FPU(s) 634 for execution. Each execution thread can access 128 general-purpose registers within the GRF 624, where each register can store 32 bytes, accessible as a SIMD 8-element vector of 32-bit data elements. In one embodiment, each execution unit thread has access to 4 Kbytes within the GRF 624, although embodiments are not so limited, and greater or fewer register resources may be provided in other embodiments. In one embodiment up to seven threads can execute simultaneously, although the number of threads per execution unit can also vary according to embodiments. In an embodiment in which seven threads may access 4 Kbytes, the GRF 624 can store a total of 28 Kbytes. Flexible addressing modes can permit registers to be addressed together to build effectively wider registers or to represent strided rectangular block data structures.

In one embodiment, memory operations, sampler operations, and other longer-latency system communications are dispatched via “send” instructions that are executed by the message passing send unit 630. In one embodiment, branch instructions are dispatched to a dedicated branch unit 632 to facilitate SIMD divergence and eventual convergence.

In one embodiment the graphics execution unit 608 includes one or more SIMD floating-point units (FPU(s)) 634 to perform floating-point operations. In one embodiment, the FPU(s) 634 also support integer computation. In one embodiment the FPU(s) 634 can SIMD execute up to M number of 32-bit floating-point (or integer) operations, or SIMD execute up to 2M 16-bit integer or 16-bit floating-point operations. In one embodiment, at least one of the FPU(s) provides extended math capability to support high-throughput transcendental math functions and double precision 64-bit floating-point. In some embodiments, a set of 8-bit integer SIMD ALUs 635 are also present, and may be specifically optimized to perform operations associated with machine learning computations.

In one embodiment, arrays of multiple instances of the graphics execution unit 608 can be instantiated in a graphics sub-core grouping (e.g., a sub-slice). For scalability, product architects can chose the exact number of execution units per sub-core grouping. In one embodiment the execution unit 608 can execute instructions across a plurality of execution channels. In a further embodiment, each thread executed on the graphics execution unit 608 is executed on a different channel.

FIG. 7 is a block diagram illustrating a graphics processor instruction formats 700 according to some embodiments. In one or more embodiment, the graphics processor execution units support an instruction set having instructions in multiple formats. The solid lined boxes illustrate the components that are generally included in an execution unit instruction, while the dashed lines include components that are optional or that are only included in a sub-set of the instructions. In some embodiments, instruction format 700 described and illustrated are macro-instructions, in that they are instructions supplied to the execution unit, as opposed to micro-operations resulting from instruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units natively support instructions in a 128-bit instruction format 710. A 64-bit compacted instruction format 730 is available for some instructions based on the selected instruction, instruction options, and number of operands. The native 128-bit instruction format 710 provides access to all instruction options, while some options and operations are restricted in the 64-bit format 730. The native instructions available in the 64-bit format 730 vary by embodiment. In some embodiments, the instruction is compacted in part using a set of index values in an index field 713. The execution unit hardware references a set of compaction tables based on the index values and uses the compaction table outputs to reconstruct a native instruction in the 128-bit instruction format 710.

For each format, instruction opcode 712 defines the operation that the execution unit is to perform. The execution units execute each instruction in parallel across the multiple data elements of each operand. For example, in response to an add instruction the execution unit performs a simultaneous add operation across each color channel representing a texture element or picture element. By default, the execution unit performs each instruction across all data channels of the operands. In some embodiments, instruction control field 714 enables control over certain execution options, such as channels selection (e.g., predication) and data channel order (e.g., swizzle). For instructions in the 128-bit instruction format 710 an exec-size field 716 limits the number of data channels that will be executed in parallel. In some embodiments, exec-size field 716 is not available for use in the 64-bit compact instruction format 730.

Some execution unit instructions have up to three operands including two source operands, src0 720, src1 722, and one destination 718. In some embodiments, the execution units support dual destination instructions, where one of the destinations is implied. Data manipulation instructions can have a third source operand (e.g., SRC2 724), where the instruction opcode 712 determines the number of source operands. An instruction's last source operand can be an immediate (e.g., hard-coded) value passed with the instruction.

In some embodiments, the 128-bit instruction format 710 includes an access/address mode field 726 specifying, for example, whether direct register addressing mode or indirect register addressing mode is used. When direct register addressing mode is used, the register address of one or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 710 includes an access/address mode field 726, which specifies an address mode and/or an access mode for the instruction. In one embodiment the access mode is used to define a data access alignment for the instruction. Some embodiments support access modes including a 16-byte aligned access mode and a 1-byte aligned access mode, where the byte alignment of the access mode determines the access alignment of the instruction operands. For example, when in a first mode, the instruction may use byte-aligned addressing for source and destination operands and when in a second mode, the instruction may use 16-byte-aligned addressing for all source and destination operands.

In one embodiment, the address mode portion of the access/address mode field 726 determines whether the instruction is to use direct or indirect addressing. When direct register addressing mode is used bits in the instruction directly provide the register address of one or more operands. When indirect register addressing mode is used, the register address of one or more operands may be computed based on an address register value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 712 bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4, 5, and 6 allow the execution unit to determine the type of opcode. The precise opcode grouping shown is merely an example. In some embodiments, a move and logic opcode group 742 includes data movement and logic instructions (e.g., move (mov), compare (cmp)). In some embodiments, move and logic group 742 shares the five most significant bits (MSB), where move (mov) instructions are in the form of 0000xxxxb and logic instructions are in the form of 0001xxxxb. A flow control instruction group 744 (e.g., call, jump (jmp)) includes instructions in the form of 0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes a mix of instructions, including synchronization instructions (e.g., wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instruction group 748 includes component-wise arithmetic instructions (e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel math group 748 performs the arithmetic operations in parallel across data channels. The vector math group 750 includes arithmetic instructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math group performs arithmetic such as dot product calculations on vector operands.

Graphics Pipeline

FIG. 8 is a block diagram of another embodiment of a graphics processor 800. Elements of FIG. 8 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such.

In some embodiments, graphics processor 800 includes a geometry pipeline 820, a media pipeline 830, a display engine 840, thread execution logic 850, and a render output pipeline 870. In some embodiments, graphics processor 800 is a graphics processor within a multi-core processing system that includes one or more general purpose processing cores. The graphics processor is controlled by register writes to one or more control registers (not shown) or via commands issued to graphics processor 800 via a ring interconnect 802. In some embodiments, ring interconnect 802 couples graphics processor 800 to other processing components, such as other graphics processors or general-purpose processors. Commands from ring interconnect 802 are interpreted by a command streamer 803, which supplies instructions to individual components of the geometry pipeline 820 or the media pipeline 830.

In some embodiments, command streamer 803 directs the operation of a vertex fetcher 805 that reads vertex data from memory and executes vertex-processing commands provided by command streamer 803. In some embodiments, vertex fetcher 805 provides vertex data to a vertex shader 807, which performs coordinate space transformation and lighting operations to each vertex. In some embodiments, vertex fetcher 805 and vertex shader 807 execute vertex-processing instructions by dispatching execution threads to execution units 852A-852B via a thread dispatcher 831.

In some embodiments, execution units 852A-852B are an array of vector processors having an instruction set for performing graphics and media operations. In some embodiments, execution units 852A-852B have an attached L1 cache 851 that is specific for each array or shared between the arrays. The cache can be configured as a data cache, an instruction cache, or a single cache that is partitioned to contain data and instructions in different partitions.

In some embodiments, geometry pipeline 820 includes tessellation components to perform hardware-accelerated tessellation of 3D objects. In some embodiments, a programmable hull shader 811 configures the tessellation operations. A programmable domain shader 817 provides back-end evaluation of tessellation output. A tessellator 813 operates at the direction of hull shader 811 and contains special purpose logic to generate a set of detailed geometric objects based on a coarse geometric model that is provided as input to geometry pipeline 820. In some embodiments, if tessellation is not used, tessellation components (e.g., hull shader 811, tessellator 813, and domain shader 817) can be bypassed.

In some embodiments, complete geometric objects can be processed by a geometry shader 819 via one or more threads dispatched to execution units 852A-852B, or can proceed directly to the clipper 829. In some embodiments, the geometry shader operates on entire geometric objects, rather than vertices or patches of vertices as in previous stages of the graphics pipeline. If the tessellation is disabled the geometry shader 819 receives input from the vertex shader 807. In some embodiments, geometry shader 819 is programmable by a geometry shader program to perform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 829 can process vertex data. The clipper 829 may be a fixed function clipper or a programmable clipper having clipping and geometry shader functions. In some embodiments, a rasterizer and depth test component 873 in the render output pipeline 870 dispatches pixel shaders to convert the geometric objects into per pixel representations. In some embodiments, pixel shader logic is included in thread execution logic 850. In some embodiments, an application can bypass the rasterizer and depth test component 873 and access un-rasterized vertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric, or some other interconnect mechanism that allows data and message passing amongst the major components of the processor. In some embodiments, execution units 852A-852B and associated logic units (e.g., L1 cache 851, sampler 854, texture cache 858, etc.) interconnect via a data port 856 to perform memory access and communicate with render output pipeline components of the processor. In some embodiments, sampler 854, caches 851, 858 and execution units 852A-852B each have separate memory access paths. In one embodiment the texture cache 858 can also be configured as a sampler cache.

In some embodiments, render output pipeline 870 contains a rasterizer and depth test component 873 that converts vertex-based objects into an associated pixel-based representation. In some embodiments, the rasterizer logic includes a windower/masker unit to perform fixed function triangle and line rasterization. An associated render cache 878 and depth cache 879 are also available in some embodiments. A pixel operations component 877 performs pixel-based operations on the data, though in some instances, pixel operations associated with 2D operations (e.g. bit block image transfers with blending) are performed by the 2D engine 841, or substituted at display time by the display controller 843 using overlay display planes. In some embodiments, a shared L3 cache 875 is available to all graphics components, allowing the sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes a media engine 837 and a video front-end 834. In some embodiments, video front-end 834 receives pipeline commands from the command streamer 803. In some embodiments, media pipeline 830 includes a separate command streamer. In some embodiments, video front-end 834 processes media commands before sending the command to the media engine 837. In some embodiments, media engine 837 includes thread spawning functionality to spawn threads for dispatch to thread execution logic 850 via thread dispatcher 831.

In some embodiments, graphics processor 800 includes a display engine 840. In some embodiments, display engine 840 is external to processor 800 and couples with the graphics processor via the ring interconnect 802, or some other interconnect bus or fabric. In some embodiments, display engine 840 includes a 2D engine 841 and a display controller 843. In some embodiments, display engine 840 contains special purpose logic capable of operating independently of the 3D pipeline. In some embodiments, display controller 843 couples with a display device (not shown), which may be a system integrated display device, as in a laptop computer, or an external display device attached via a display device connector.

In some embodiments, the geometry pipeline 820 and media pipeline 830 are configurable to perform operations based on multiple graphics and media programming interfaces and are not specific to any one application programming interface (API). In some embodiments, driver software for the graphics processor translates API calls that are specific to a particular graphics or media library into commands that can be processed by the graphics processor. In some embodiments, support is provided for the Open Graphics Library (OpenGL), Open Computing Language (OpenCL), and/or Vulkan graphics and compute API, all from the Khronos Group. In some embodiments, support may also be provided for the Direct3D library from the Microsoft Corporation. In some embodiments, a combination of these libraries may be supported. Support may also be provided for the Open Source Computer Vision Library (OpenCV). A future API with a compatible 3D pipeline would also be supported if a mapping can be made from the pipeline of the future API to the pipeline of the graphics processor.

Graphics Pipeline Programming

FIG. 9A is a block diagram illustrating a graphics processor command format 900 according to some embodiments. FIG. 9B is a block diagram illustrating a graphics processor command sequence 910 according to an embodiment. The solid lined boxes in FIG. 9A illustrate the components that are generally included in a graphics command while the dashed lines include components that are optional or that are only included in a sub-set of the graphics commands. The exemplary graphics processor command format 900 of FIG. 9A includes data fields to identify a client 902, a command operation code (opcode) 904, and data 906 for the command. A sub-opcode 905 and a command size 908 are also included in some commands.

In some embodiments, client 902 specifies the client unit of the graphics device that processes the command data. In some embodiments, a graphics processor command parser examines the client field of each command to condition the further processing of the command and route the command data to the appropriate client unit. In some embodiments, the graphics processor client units include a memory interface unit, a render unit, a 2D unit, a 3D unit, and a media unit. Each client unit has a corresponding processing pipeline that processes the commands. Once the command is received by the client unit, the client unit reads the opcode 904 and, if present, sub-opcode 905 to determine the operation to perform. The client unit performs the command using information in data field 906. For some commands an explicit command size 908 is expected to specify the size of the command. In some embodiments, the command parser automatically determines the size of at least some of the commands based on the command opcode. In some embodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 9B illustrates an exemplary graphics processor command sequence 910. In some embodiments, software or firmware of a data processing system that features an embodiment of a graphics processor uses a version of the command sequence shown to set up, execute, and terminate a set of graphics operations. A sample command sequence is shown and described for purposes of example only as embodiments are not limited to these specific commands or to this command sequence. Moreover, the commands may be issued as batch of commands in a command sequence, such that the graphics processor will process the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 may begin with a pipeline flush command 912 to cause any active graphics pipeline to complete the currently pending commands for the pipeline. In some embodiments, the 3D pipeline 922 and the media pipeline 924 do not operate concurrently. The pipeline flush is performed to cause the active graphics pipeline to complete any pending commands In response to a pipeline flush, the command parser for the graphics processor will pause command processing until the active drawing engines complete pending operations and the relevant read caches are invalidated. Optionally, any data in the render cache that is marked ‘dirty’ can be flushed to memory. In some embodiments, pipeline flush command 912 can be used for pipeline synchronization or before placing the graphics processor into a low power state.

In some embodiments, a pipeline select command 913 is used when a command sequence requires the graphics processor to explicitly switch between pipelines. In some embodiments, a pipeline select command 913 is required only once within an execution context before issuing pipeline commands unless the context is to issue commands for both pipelines. In some embodiments, a pipeline flush command 912 is required immediately before a pipeline switch via the pipeline select command 913.

In some embodiments, a pipeline control command 914 configures a graphics pipeline for operation and is used to program the 3D pipeline 922 and the media pipeline 924. In some embodiments, pipeline control command 914 configures the pipeline state for the active pipeline. In one embodiment, the pipeline control command 914 is used for pipeline synchronization and to clear data from one or more cache memories within the active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 916 are used to configure a set of return buffers for the respective pipelines to write data. Some pipeline operations require the allocation, selection, or configuration of one or more return buffers into which the operations write intermediate data during processing. In some embodiments, the graphics processor also uses one or more return buffers to store output data and to perform cross thread communication. In some embodiments, the return buffer state 916 includes selecting the size and number of return buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on the active pipeline for operations. Based on a pipeline determination 920, the command sequence is tailored to the 3D pipeline 922 beginning with the 3D pipeline state 930 or the media pipeline 924 beginning at the media pipeline state 940.

The commands to configure the 3D pipeline state 930 include 3D state setting commands for vertex buffer state, vertex element state, constant color state, depth buffer state, and other state variables that are to be configured before 3D primitive commands are processed. The values of these commands are determined at least in part based on the particular 3D API in use. In some embodiments, 3D pipeline state 930 commands are also able to selectively disable or bypass certain pipeline elements if those elements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3D primitives to be processed by the 3D pipeline. Commands and associated parameters that are passed to the graphics processor via the 3D primitive 932 command are forwarded to the vertex fetch function in the graphics pipeline. The vertex fetch function uses the 3D primitive 932 command data to generate vertex data structures. The vertex data structures are stored in one or more return buffers. In some embodiments, 3D primitive 932 command is used to perform vertex operations on 3D primitives via vertex shaders. To process vertex shaders, 3D pipeline 922 dispatches shader execution threads to graphics processor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934 command or event. In some embodiments, a register write triggers command execution. In some embodiments execution is triggered via a ‘go’ or ‘kick’ command in the command sequence. In one embodiment, command execution is triggered using a pipeline synchronization command to flush the command sequence through the graphics pipeline. The 3D pipeline will perform geometry processing for the 3D primitives. Once operations are complete, the resulting geometric objects are rasterized and the pixel engine colors the resulting pixels. Additional commands to control pixel shading and pixel back end operations may also be included for those operations.

In some embodiments, the graphics processor command sequence 910 follows the media pipeline 924 path when performing media operations. In general, the specific use and manner of programming for the media pipeline 924 depends on the media or compute operations to be performed. Specific media decode operations may be offloaded to the media pipeline during media decode. In some embodiments, the media pipeline can also be bypassed and media decode can be performed in whole or in part using resources provided by one or more general purpose processing cores. In one embodiment, the media pipeline also includes elements for general-purpose graphics processor unit (GPGPU) operations, where the graphics processor is used to perform SIMD vector operations using computational shader programs that are not explicitly related to the rendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similar manner as the 3D pipeline 922. A set of commands to configure the media pipeline state 940 are dispatched or placed into a command queue before the media object commands 942. In some embodiments, commands for the media pipeline state 940 include data to configure the media pipeline elements that will be used to process the media objects. This includes data to configure the video decode and video encode logic within the media pipeline, such as encode or decode format. In some embodiments, commands for the media pipeline state 940 also support the use of one or more pointers to “indirect” state elements that contain a batch of state settings.

In some embodiments, media object commands 942 supply pointers to media objects for processing by the media pipeline. The media objects include memory buffers containing video data to be processed. In some embodiments, all media pipeline states must be valid before issuing a media object command 942. Once the pipeline state is configured and media object commands 942 are queued, the media pipeline 924 is triggered via an execute command 944 or an equivalent execute event (e.g., register write). Output from media pipeline 924 may then be post processed by operations provided by the 3D pipeline 922 or the media pipeline 924. In some embodiments, GPGPU operations are configured and executed in a similar manner as media operations.

Graphics Software Architecture

FIG. 10 illustrates exemplary graphics software architecture for a data processing system 1000 according to some embodiments. In some embodiments, software architecture includes a 3D graphics application 1010, an operating system 1020, and at least one processor 1030. In some embodiments, processor 1030 includes a graphics processor 1032 and one or more general-purpose processor core(s) 1034. The graphics application 1010 and operating system 1020 each execute in the system memory 1050 of the data processing system.

In some embodiments, 3D graphics application 1010 contains one or more shader programs including shader instructions 1012. The shader language instructions may be in a high-level shader language, such as the High Level Shader Language (HLSL) or the OpenGL Shader Language (GLSL). The application also includes executable instructions 1014 in a machine language suitable for execution by the general-purpose processor core 1034. The application also includes graphics objects 1016 defined by vertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows® operating system from the Microsoft Corporation, a proprietary UNIX-like operating system, or an open source UNIX-like operating system using a variant of the Linux kernel. The operating system 1020 can support a graphics API 1022 such as the Direct3D API, the OpenGL API, or the Vulkan API. When the Direct3D API is in use, the operating system 1020 uses a front-end shader compiler 1024 to compile any shader instructions 1012 in HLSL into a lower-level shader language. The compilation may be a just-in-time (JIT) compilation or the application can perform shader pre-compilation. In some embodiments, high-level shaders are compiled into low-level shaders during the compilation of the 3D graphics application 1010. In some embodiments, the shader instructions 1012 are provided in an intermediate form, such as a version of the Standard Portable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1026 contains a back-end shader compiler 1027 to convert the shader instructions 1012 into a hardware specific representation. When the OpenGL API is in use, shader instructions 1012 in the GLSL high-level language are passed to a user mode graphics driver 1026 for compilation. In some embodiments, user mode graphics driver 1026 uses operating system kernel mode functions 1028 to communicate with a kernel mode graphics driver 1029. In some embodiments, kernel mode graphics driver 1029 communicates with graphics processor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented by representative code stored on a machine-readable medium which represents and/or defines logic within an integrated circuit such as a processor. For example, the machine-readable medium may include instructions which represent various logic within the processor. When read by a machine, the instructions may cause the machine to fabricate the logic to perform the techniques described herein. Such representations, known as “IP cores,” are reusable units of logic for an integrated circuit that may be stored on a tangible, machine-readable medium as a hardware model that describes the structure of the integrated circuit. The hardware model may be supplied to various customers or manufacturing facilities, which load the hardware model on fabrication machines that manufacture the integrated circuit. The integrated circuit may be fabricated such that the circuit performs operations described in association with any of the embodiments described herein.

FIG. 11A is a block diagram illustrating an IP core development system 1100 that may be used to manufacture an integrated circuit to perform operations according to an embodiment. The IP core development system 1100 may be used to generate modular, reusable designs that can be incorporated into a larger design or used to construct an entire integrated circuit (e.g., an SOC integrated circuit). A design facility 1130 can generate a software simulation 1110 of an IP core design in a high-level programming language (e.g., C/C++). The software simulation 1110 can be used to design, test, and verify the behavior of the IP core using a simulation model 1112. The simulation model 1112 may include functional, behavioral, and/or timing simulations. A register transfer level (RTL) design 1115 can then be created or synthesized from the simulation model 1112. The RTL design 1115 is an abstraction of the behavior of the integrated circuit that models the flow of digital signals between hardware registers, including the associated logic performed using the modeled digital signals. In addition to an RTL design 1115, lower-level designs at the logic level or transistor level may also be created, designed, or synthesized. Thus, the particular details of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by the design facility into a hardware model 1120, which may be in a hardware description language (HDL), or some other representation of physical design data. The HDL may be further simulated or tested to verify the IP core design. The IP core design can be stored for delivery to a 3^(rd) party fabrication facility 1165 using non-volatile memory 1140 (e.g., hard disk, flash memory, or any non-volatile storage medium). Alternatively, the IP core design may be transmitted (e.g., via the Internet) over a wired connection 1150 or wireless connection 1160. The fabrication facility 1165 may then fabricate an integrated circuit that is based at least in part on the IP core design. The fabricated integrated circuit can be configured to perform operations in accordance with at least one embodiment described herein.

FIG. 11B illustrates a cross-section side view of an integrated circuit package assembly 1170, according to some embodiments described herein. The integrated circuit package assembly 1170 illustrates an implementation of one or more processor or accelerator devices as described herein. The package assembly 1170 includes multiple units of hardware logic 1172, 1174 connected to a substrate 1180. The logic 1172, 1174 may be implemented at least partly in configurable logic or fixed-functionality logic hardware, and can include one or more portions of any of the processor core(s), graphics processor(s), or other accelerator devices described herein. Each unit of logic 1172, 1174 can be implemented within a semiconductor die and coupled with the substrate 1180 via an interconnect structure 1173. The interconnect structure 1173 may be configured to route electrical signals between the logic 1172, 1174 and the substrate 1180, and can include interconnects such as, but not limited to bumps or pillars. In some embodiments, the interconnect structure 1173 may be configured to route electrical signals such as, for example, input/output (110) signals and/or power or ground signals associated with the operation of the logic 1172, 1174. In some embodiments, the substrate 1180 is an epoxy-based laminate substrate. The package substrate 1180 may include other suitable types of substrates in other embodiments. The package assembly 1170 can be connected to other electrical devices via a package interconnect 1183. The package interconnect 1183 may be coupled to a surface of the substrate 1180 to route electrical signals to other electrical devices, such as a motherboard, other chipset, or multi-chip module.

In some embodiments, the units of logic 1172, 1174 are electrically coupled with a bridge 1182 that is configured to route electrical signals between the logic 1172, 1174. The bridge 1182 may be a dense interconnect structure that provides a route for electrical signals. The bridge 1182 may include a bridge substrate composed of glass or a suitable semiconductor material. Electrical routing features can be formed on the bridge substrate to provide a chip-to-chip connection between the logic 1172, 1174.

Although two units of logic 1172, 1174 and a bridge 1182 are illustrated, embodiments described herein may include more or fewer logic units on one or more dies. The one or more dies may be connected by zero or more bridges, as the bridge 1182 may be excluded when the logic is included on a single die. Alternatively, multiple dies or units of logic can be connected by one or more bridges. Additionally, multiple logic units, dies, and bridges can be connected together in other possible configurations, including three-dimensional configurations.

Exemplary System on a Chip Integrated Circuit

FIGS. 12-14 illustrated exemplary integrated circuits and associated graphics processors that may be fabricated using one or more IP cores, according to various embodiments described herein. In addition to what is illustrated, other logic and circuits may be included, including additional graphics processors/cores, peripheral interface controllers, or general-purpose processor cores.

FIG. 12 is a block diagram illustrating an exemplary system on a chip integrated circuit 1200 that may be fabricated using one or more IP cores, according to an embodiment. Exemplary integrated circuit 1200 includes one or more application processor(s) 1205 (e.g., CPUs), at least one graphics processor 1210, and may additionally include an image processor 1215 and/or a video processor 1220, any of which may be a modular IP core from the same or multiple different design facilities. Integrated circuit 1200 includes peripheral or bus logic including a USB controller 1225, UART controller 1230, an SPI/SDIO controller 1235, and an I²S/I²C controller 1240. Additionally, the integrated circuit can include a display device 1245 coupled to one or more of a high-definition multimedia interface (HDMI) controller 1250 and a mobile industry processor interface (MIPI) display interface 1255. Storage may be provided by a flash memory subsystem 1260 including flash memory and a flash memory controller. Memory interface may be provided via a memory controller 1265 for access to SDRAM or SRAM memory devices. Some integrated circuits additionally include an embedded security engine 1270.

FIGS. 13A-13B are block diagrams illustrating exemplary graphics processors for use within an SoC, according to embodiments described herein. FIG. 13A illustrates an exemplary graphics processor 1310 of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment. FIG. 13B illustrates an additional exemplary graphics processor 1340 of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment. Graphics processor 1310 of FIG. 13A is an example of a low power graphics processor core. Graphics processor 1340 of FIG. 13B is an example of a higher performance graphics processor core. Each of the graphics processors 1310, 1340 can be variants of the graphics processor 1210 of FIG. 12.

As shown in FIG. 13A, graphics processor 1310 includes a vertex processor 1305 and one or more fragment processor(s) 1315A-1315N (e.g., 1315A, 1315B, 1315C, 1315D, through 1315N-1, and 1315N). Graphics processor 1310 can execute different shader programs via separate logic, such that the vertex processor 1305 is optimized to execute operations for vertex shader programs, while the one or more fragment processor(s) 1315A-1315N execute fragment (e.g., pixel) shading operations for fragment or pixel shader programs. The vertex processor 1305 performs the vertex processing stage of the 3D graphics pipeline and generates primitives and vertex data. The fragment processor(s) 1315A-1315N use the primitive and vertex data generated by the vertex processor 1305 to produce a framebuffer that is displayed on a display device. In one embodiment, the fragment processor(s) 1315A-1315N are optimized to execute fragment shader programs as provided for in the OpenGL API, which may be used to perform similar operations as a pixel shader program as provided for in the Direct 3D API.

Graphics processor 1310 additionally includes one or more memory management units (MMUs) 1320A-1320B, cache(s) 1325A-1325B, and circuit interconnect(s) 1330A-1330B. The one or more MMU(s) 1320A-1320B provide for virtual to physical address mapping for the graphics processor 1310, including for the vertex processor 1305 and/or fragment processor(s) 1315A-1315N, which may reference vertex or image/texture data stored in memory, in addition to vertex or image/texture data stored in the one or more cache(s) 1325A-1325B. In one embodiment, the one or more MMU(s) 1320A-1320B may be synchronized with other MMUs within the system, including one or more MMUs associated with the one or more application processor(s) 1205, image processor 1215, and/or video processor 1220 of FIG. 12, such that each processor 1205-1220 can participate in a shared or unified virtual memory system. The one or more circuit interconnect(s) 1330A-1330B enable graphics processor 1310 to interface with other IP cores within the SoC, either via an internal bus of the SoC or via a direct connection, according to embodiments.

As shown FIG. 13B, graphics processor 1340 includes the one or more MMU(s) 1320A-1320B, caches 1325A-1325B, and circuit interconnects 1330A-1330B of the graphics processor 1310 of FIG. 13A. Graphics processor 1340 includes one or more shader core(s) 1355A-1355N (e.g., 1455A, 1355B, 1355C, 1355D, 1355E, 1355F, through 1355N-1, and 1355N), which provides for a unified shader core architecture in which a single core or type or core can execute all types of programmable shader code, including shader program code to implement vertex shaders, fragment shaders, and/or compute shaders. The exact number of shader cores present can vary among embodiments and implementations. Additionally, graphics processor 1340 includes an inter-core task manager 1345, which acts as a thread dispatcher to dispatch execution threads to one or more shader cores 1355A-1355N and a tiling unit 1358 to accelerate tiling operations for tile-based rendering, in which rendering operations for a scene are subdivided in image space, for example to exploit local spatial coherence within a scene or to optimize use of internal caches.

FIGS. 14A-14B illustrate additional exemplary graphics processor logic according to embodiments described herein. FIG. 14A illustrates a graphics core 1400 that may be included within the graphics processor 1210 of FIG. 12, and may be a unified shader core 1355A-1355N as in FIG. 13B. FIG. 14B illustrates a highly-parallel general-purpose graphics processing unit 1430 suitable for deployment on a multi-chip module.

As shown in FIG. 14A, the graphics core 1400 includes a shared instruction cache 1402, a texture unit 1418, and a cache/shared memory 1420 that are common to the execution resources within the graphics core 1400. The graphics core 1400 can include multiple slices 1401A-1401N or partition for each core, and a graphics processor can include multiple instances of the graphics core 1400. The slices 1401A-1401N can include support logic including a local instruction cache 1404A-1404N, a thread scheduler 1406A-1406N, a thread dispatcher 1408A-1408N, and a set of registers 1410A. To perform logic operations, the slices 1401A-1401N can include a set of additional function units (AFUs 1412A-1412N), floating-point units (FPU 1414A-1414N), integer arithmetic logic units (ALUs 1416-1416N), address computational units (ACU 1413A-1413N), double-precision floating-point units (DPFPU 1415A-1415N), and matrix processing units (MPU 1417A-1417N).

Some of the computational units operate at a specific precision. For example, the FPUs 1414A-1414N can perform single-precision (32-bit) and half-precision (16-bit) floating-point operations, while the DPFPUs 1415A-1415N perform double precision (64-bit) floating-point operations. The ALUs 1416A-1416N can perform variable precision integer operations at 8-bit, 16-bit, and 32-bit precision, and can be configured for mixed precision operations. The MPUs 1417A-1417N can also be configured for mixed precision matrix operations, including half-precision floating-point and 8-bit integer operations. The MPUs 1417-1417N can perform a variety of matrix operations to accelerate machine learning application frameworks, including enabling support for accelerated general matrix to matrix multiplication (GEMM). The AFUs 1412A-1412N can perform additional logic operations not supported by the floating-point or integer units, including trigonometric operations (e.g., Sine, Cosine, etc.).

As shown in FIG. 14B, a general-purpose processing unit (GPGPU) 1430 can be configured to enable highly-parallel compute operations to be performed by an array of graphics processing units. Additionally, the GPGPU 1430 can be linked directly to other instances of the GPGPU to create a multi-GPU cluster to improve training speed for particularly deep neural networks. The GPGPU 1430 includes a host interface 1432 to enable a connection with a host processor. In one embodiment the host interface 1432 is a PCI Express interface. However, the host interface can also be a vendor specific communications interface or communications fabric. The GPGPU 1430 receives commands from the host processor and uses a global scheduler 1434 to distribute execution threads associated with those commands to a set of compute clusters 1436A-1436H. The compute clusters 1436A-1436H share a cache memory 1438. The cache memory 1438 can serve as a higher-level cache for cache memories within the compute clusters 1436A-1436H.

The GPGPU 1430 includes memory 1434A-1434B coupled with the compute clusters 1436A-1436H via a set of memory controllers 1442A-1442B. In various embodiments, the memory 1434A-1434B can include various types of memory devices including dynamic random-access memory (DRAM) or graphics random access memory, such as synchronous graphics random access memory (SGRAM), including graphics double data rate (GDDR) memory.

In one embodiment, the compute clusters 1436A-1436H each include a set of graphics cores, such as the graphics core 1400 of FIG. 14A, which can include multiple types of integer and floating-point logic units that can perform computational operations at a range of precisions including suited for machine learning computations. For example and in one embodiment at least a subset of the floating-point units in each of the compute clusters 1436A-1436H can be configured to perform 16-bit or 32-bit floating-point operations, while a different subset of the floating-point units can be configured to perform 64-bit floating-point operations.

Multiple instances of the GPGPU 1430 can be configured to operate as a compute cluster. The communication mechanism used by the compute cluster for synchronization and data exchange varies across embodiments. In one embodiment, the multiple instances of the GPGPU 1430 communicate over the host interface 1432. In one embodiment, the GPGPU 1430 includes an I/O hub 1439 that couples the GPGPU 1430 with a GPU link 1440 that enables a direct connection to other instances of the GPGPU. In one embodiment, the GPU link 1440 is coupled to a dedicated GPU-to-GPU bridge that enables communication and synchronization between multiple instances of the GPGPU 1430. In one embodiment, the GPU link 1440 couples with a high-speed interconnect to transmit and receive data to other GPGPUs or parallel processors. In one embodiment the multiple instances of the GPGPU 1430 are located in separate data processing systems and communicate via a network device that is accessible via the host interface 1432. In one embodiment the GPU link 1440 can be configured to enable a connection to a host processor in addition to or as an alternative to the host interface 1432.

While the illustrated configuration of the GPGPU 1430 can be configured to train neural networks, one embodiment provides alternate configuration of the GPGPU 1430 that can be configured for deployment within a high performance or low power inferencing platform. In an inferencing configuration the GPGPU 1430 includes fewer of the compute clusters 1436A-1436H relative to the training configuration. Additionally, the memory technology associated with the memory 1434A-1434B may differ between inferencing and training configurations, with higher bandwidth memory technologies devoted to training configurations. In one embodiment the inferencing configuration of the GPGPU 1430 can support inferencing specific instructions. For example, an inferencing configuration can provide support for one or more 8-bit integer dot product instructions, which are commonly used during inferencing operations for deployed neural networks.

Encoding and Decoding Feature Maps and Weights for a Convolutional Neural Network

Embodiments described herein provide hardware logic to compress CNN kernel data in main memory via the integration of low-complexity and low-latency decoder logic within the computation blocks of graphic processing logic. A real-time encoder is configured to compress output feature maps and write the corresponding compressed data to system memory. At read time, a real-time decode is configured to decompress the data before the data is consumed by the computation unit. The techniques described herein can both reduce the memory footprint of CNN feature maps in memory, reduce required transmission bandwidth, and reduce the power required to read and write CNN feature maps to and from memory.

FIG. 15 is an illustration of primitive elements of a convolutional neural network. A convolutional neural network (CNN) includes a number of convolution and subsampling layers optionally followed by one or more fully connected layers. The convolutional layers are the core building blocks of a CNN and are where the majority of the computational operations of the CNN are performed. Within a convolutional layer, an original image 1502 having some data to be analyzed is processed by a set of convolution kernels that apply each apply a different filter 1504A, 1504B to the original image 1502. The filters 1504A, 1504B are learnable and typically much smaller than the original image to which the filters will be applied. The convolution kernels output a set of feature maps 1506A, 1506B that contain the features searched for by the convolution kernels. Feature map data output from one of the convolutional layers can be used as inputs to filter operations in subsequent convolutional layers. Results of the filter operations may be summed together to provide an output from the convolutional layer to a subsequent layer, such as a pooling layer or a fully connected neural network.

FIG. 16A-B are illustrations of a conventional implementation of a convolutional neural network 1600. The convolutional neural network 1600 illustrated in FIG. 16A analyzes red, green, and blue (RGB) components 1602 of an image. In general, a CNN can be used to analyze an m×m×r image, where m is the height and width of the image and r is the number of channels. For example, an RGB image has r=3 channels. The first layer in a CNN is a convolutional layer, with convolutional neural network 1600 illustrated as having multiple convolutional layers 1604A, 1604B. The convolutional layers 1604A, 1604B can have k filters (or kernels) of size n×n×q, where n is smaller than the dimension of the image and q can either be the same as the number of channels r or smaller, and may vary for each kernel. The size of the filters gives rise to the locally connected structure and are each convolved with the image to produce k feature maps of size m−n+1. Some implementations use convolution kernels in pixel sizes that are each much smaller than the input images or feature maps sizes (e.g., 11*11, 9*9, 7*7, 5*5, 3*3 and/or 1*1), although the specific feature map size can be tailored to the specific requirements of the implementation.

The convolutional layers 1604A, 1604B can feed into one or more fully connected layers 1606. The high-level reasoning in the neural network can be performed in the fully connected layers 1606, as neurons in a fully connected layer have full connections to all activations in the previous layer. The output of the fully connected layers 1606 is generally a classification result based on the high level features of the input image.

FIG. 16B illustrates a conventional CNN implementation, in which the size of a feature map 1612 can be defined as (m*m), the size of a convolution kernel 1614 is defined as (n*n), producing a convolution result 1616 having a size of (m−n+1)*(m−n+1) without padding zeros rounding the input, where m is significantly greater than n (e.g., (m>>n)). In some implementations the convolution result can be further reduced using down-sampling or pooling layers, which can shrink the original input image to multiple feature maps with sizes of less than 7*7 pixels, as in some instances a highly compact feature map can be more information intensive.

FIGS. 17A-B illustrate memory layouts for feature maps and kernel data, according to an embodiment. FIG. 17A illustrates an exemplary memory layout for a set of feature block units, including exemplary feature block unit 1702A through feature block unit 1702N. The feature block units (e.g., feature block unit 1702A-1702N) are each independently decodable units of feature maps, such as the feature maps 1506A, 1506B as in FIG. 15. The feature block units for a layer operate on an input feature map 1711 to generate an output feature map 1721. The output feature map 1721 is then used as an input feature map for a successive layer. The feature block units can be generated by convolution kernels in a first convolution layer and used as input to convolution kernels in a second convolutional layer, as illustrated by convolutional layers 1604A, 1604B of FIG. 16A. A feature block unit can be an individual feature map, a partial feature map, or a combination of multiple feature maps. Each feature block unit can be compressed by the compression techniques described herein.

FIG. 17B illustrate an exemplary memory layout for kernel block units 1712A through 1712N. The kernel block units (e.g., kernel block unit 1712A-1712N) are each independently decodable units of convolution kernels, weights, and/or parameters that are used in the computations of the various layers of a CNN. Compression techniques described herein can be applied on the granularity of an individual kernel block unit. Each kernel block units can contain 16 kernels, 32 kernels, or any power of two number of kernels.

FIG. 18 illustrates an example of a memory layout with raw data relative to the proposed compressed memory layout for the kernel block units, according to an embodiment. The feature block units and kernel block units are losslessly compressed. Because lossless compression does not guarantee a compression ratio, memory can be allocated to the size of the uncompressed kernel block and metadata can be used to indicate which portions of a compressed kernel block contains valid or invalid data. For example, a set of feature block units or kernel block units (e.g., KBU/FBU 1802A, 1802B, 1802C, though 1802N) have the illustrated raw data layout in memory when in an uncompressed state. The compressed data layout for each KBU/FBU 1802A-1802N can then have a compressed data portion (e.g., compressed data 1812A, 1812B, 1812C, though 1812N) and an invalid data portion (e.g., invalid data 1814A, 1814B, 1814C, though 1814N). The sizes of the various portions can vary for each compressed kernel block unit or feature block unit. Metadata for each block of compressed data can be used to indicate which portion of a given block contains valid data.

In some embodiments, memory controllers, including direct memory access (DMA) controllers can be configured to determine the size of each FBU or KBU from metadata associated with the KBU/FBU. In one embodiment the metadata is placed at the start of the FBU/KBU. Alternatively, the metadata can be transmitted in a separate data stream that can be cached and pre-fetched by the DMA controllers. For each FBU or KBU, the DMA controller can be configured to transmit only the valid compressed portion of the FBU or KBU and bypass transmission of the invalid data.

FIG. 18 also illustrates an exemplary memory layout 1820 for a fully connected layer of a CNN, in which multiple N×N kernel block units (e.g., KBU 1822) are stored in memory in alternating bands of valid compressed data and invalid data.

FIG. 19 illustrates a compute architecture 1900 configured to enable compressed transmission of CNN data, according to an embodiment. The compute architecture 1900 can be integrated graphics processor execution logic as described herein, such as, for example, execution logic 600 of FIG. 6. The compute architecture 1900 includes, in one embodiment, a compute block 1902 and hardware scratch buffer 1904 that is coupled to memory 1908 via a DMA controller 1906. The memory 1908 can be main memory or system memory of a data processing system. The compute block 1902 can be an individual execution unit or a cluster of execution units (e.g., EU 608A-608N as in FIG. 6; fragment processors 1315A-1315N; shader cores 1415A-1415N), as well as a streaming multiprocessor, graphics processor cluster, or any collection of computing resources suitable for performing processing operations required to implement a deep neural network, such as the convolutional neural network described herein. The scratch buffer 1904 can be a high-speed on-chip memory, such as on-chip static random access memory (SRAM). In one embodiment the scratch buffer 1904 is optimized to store feature block units or kernel block units for CNN operations performed by the compute block 1902.

In one embodiment the decoder 1912 can be hardware decoder logic that is integrated into the compute block 1902 to enable compressed transmission of CNN data across the compute architecture. The compute block 1902 can generate output feature map (OFM) data in the scratch buffer 1904 in an uncompressed format. An encoder 1916 integrated into the DMA controller 1906 to enable the writing the output feature maps data to the memory 1908 in a compressed format. When the OFMs of one layer become the IFMs of the next layer, those IFMs are read from memory 1906 as compressed data 1914 and stored in the scratch buffer 1904. The decoder 1912 can enable the compute block 1902 to read in the compressed data 1914 without requiring the data to be decoded. Alternatively, a codec unit having both encode and decode logic can be integrated into the DMA controller 1906, enabling compressed data to be transmitted and read by the DMA controller 1906. The feature map data can then be decompressed by the DMA controller 1906 and written to the scratch buffer 1904 in an uncompressed format to be read by the compute block 1902.

In the embodiments described herein, the specific encoding format for kernel and feature data can be varied based on the statistics of the data to be encoded. Analysis of CNN feature map data indicates that many feature maps are highly sparse. Analysis of CNN kernel data indicates that while the kernel data is not as sparse as the feature map data, many values in the kernel data are repeated. The dynamic range of kernel data is relatively low, which indicates that raw data allocate more bits than required to store the coefficients. Using varied encoding techniques feature map and kernel data can be compressed by as much as 80% in a lossless manner using a selection of various encoding techniques.

In one embodiment, the encoding techniques include unique absolute value (UAV) table encoding, significance map (SM) encoding, table encoding (TE), unique value coordinate (UVC) encoding, and mean encoding (ME). Metadata for the encoded data indicates the type of encoding format used for the data. In one embodiment, specific encoding formats can be selected for specific types of data, such as kernel data or feature data. In one embodiment, statistical analysis is performed on the data prior to encoding to enable an appropriate encoder to be selected for each block of data.

For UAV table encoding, a number of unique absolute values for a block of encoded kernel or feature data can be encoded into a header. The specific unique absolute values can then be encoded, followed by an index map that enables each value of the bit stream to be derived from the unique absolute values. In SM encoding mode, only non-zero values in a block are encoded. The number of non-zero values in a sample block is indicated in the header, followed by a significance map indicating a map of the non-zero values within the block. The non-zero values of the sample are then encoded in order of appearance within the stream. When encoding the table of unique absolute values, a table encoding (TE) mode may be enabled when the table of unique absolute values forms an arithmetic sequence. The start of the sequence is encoded along with an offset (e.g., delta) value for the sequence, followed by the remaining unique absolute values for a sample block. In UVC encoding mode, the unique non-zero values for the block are encoded, followed by coordinates of those unique values within the stream. UVC encoding mode can be enabled when the number of unique values within a sample block is small and can be stored in a limited number of bits. As an exemplary but non-limiting example, a bit stream sample having only four unique and non-zero values can be encoded using UVC encoding. In ME encoding mode, the mean value for a sample block is encoded, followed by differential for each value from the mean value. ME encoding mode can be enabled when the values to be encoded have a limited dynamic range and are generally clustered around a mean value.

Using the encode techniques described above, a low complexity and low latency codec can be implemented that has a very low area requirement, allowing multiple instances of the decode logic to be used at various points within the processing logic, potentially enabling an encode-once, decode many system in which each logic component is configured to read encoded data. FIGS. 20-22 illustrate exemplary bit layouts for the various encoding modes. However, the explicit implementation for each mode varies across embodiments.

FIG. 20 illustrates a bit stream header 2000, according to an embodiment. The bit stream header 2000 includes a first byte 2002 and a second byte 2004. In one embodiment the first bit [0] of the first byte 2002 indicates whether the data stream to follow is compressed, with the remaining bits [7:1] of the first byte 2002 indicating the number of unique values within the block. The second byte 2004 indicates whether certain encoding modes are enabled for compressed data. For example and in one embodiment, the first bit [0] can indicate whether SM mode encoding is enabled, with the second bit [1] indicating whether TE mode encoding is enabled. The set of bits [5:2] following the first two bits can indicate a value N, where N=log₂M, and M is the number of kernel or feature blocks that are encoded in the sample block following the header. Bits [7:6] of the second byte 2004 indicates a size for the kernel associated with the bit stream.

FIG. 21 illustrates a table encoding bit stream layout 2100, according to an embodiment. The table encoding bit stream layout 2100 can used for the table encoding (TE) mode described herein, in which a series of unique absolute vales are encoded in a reduced number of bits. TE mode can also be referred to as arithmetic sequence encoding, as TE mode can be enables specifically when the set of unique absolute values includes an arithmetic sequence. In one embodiment, table encoding data is presented beginning with the third byte after a two-byte header, where the presence of table encoding data is indicated by the second bit (e.g., [1]) of the second byte 2004 of the header shown in FIG. 20. However, other embodiments are not limited to this particular configuration. The table encoding bit stream layout 2100 of FIG. 21 includes a first byte 2102, a second byte 2104, and a variable number of successive bytes 2106. The first byte 2102 contains a start of series value for the encoded arithmetic series. The second byte 2104 defines an offset for the arithmetic series that defines the difference between each value in the series. The successive bytes 2106 are variable in length and can be used to encode the remaining unique absolute values. For example, to encode a bit stream having values (0, 3, 6, 9, 12, 27, 32, 63), the values can be encoded as shown in Table 1 below.

TABLE 1 Exemplary Table Encoding Start of Series Offset Remaining UAVs 0x0 0x3 0x1B; 0x20; 0x40

As shown in Table 1, the values (0, 3, 6, 9, 12) are encoded using a start of series value of 0x0, an offset of 0x3. The metadata (e.g., bits [7:1] of the first byte 2002 in FIG. 20) indicates the total number of unique values within an encoded block, and the remaining UAV field indicates three total values, decode logic can determine the initial five values of the bit stream to be (0, 3, 6, 9, 12) using the start of series and offset values.

FIG. 22 illustrates various layouts for bit stream encoding, according to embodiments. The bit stream encoding layouts are exemplary of one embodiment, as other embodiments can be configured to use modified versions of the layouts illustrated. A first layout 2210 can be used for UAV table encoding, in which a number of unique absolute values for a block of encoded kernel or feature data can be encoded into a header 2212. The specific unique absolute values 2214 are then be encoded, followed by an index map 2216 that enables each value of the bit stream to be derived from the unique absolute values. Decode of an exemplary bit stream having the first layout 2210 is illustrated in FIG. 23.

A second layout 2220 can be used for SM encoding mode, in which the number of non-zero values in a sample block is indicated in the header 2222, followed by a significance map 2224 indicating a map of the non-zero values within the block. The non-zero values 2226 of the sample are then encoded in order of appearance within the stream. Decode of an exemplary bit stream having the second layout 2220 is illustrated in FIG. 24.

A third layout 2230 can be used for UVC encoding, where the unique values in the bit stream sample the unique values for the block are encoded, followed by coordinates of those unique values within the stream. The header 2232 can indicate the number of non-zero values that are encoded, the coordinates 2234 indicate the location (e.g., offset) of the non-zero value within the bit stream, and the set of non-zero values 2236 in the bit stream are encoded in the order of appearance within the bit stream. Decode of an exemplary bit stream having the third layout 2230 is illustrated in FIG. 25.

A fourth layout 2240 can be used for mean encoding (ME) mode, where the number of values to be encoded are indicated in the header 2242, a mean value 2244 for the segment is indicated, and the remaining values are encoded as residual values 2246. The residual values can be encoded with a reduced number of bits, as only the differences between the decoded value and the mean value is encoded. Decode of an exemplary bit stream having the fourth layout 2240 is illustrated in FIG. 26.

FIG. 23 illustrates decode of an exemplary bit stream 2310 from a bit stream having the first layout 2210 of FIG. 22. In one embodiment, the illustrated layout of FIG. 23 is used for UAV encoding. A set of unique absolute values 2214 (e.g., {0, 1.5, 2, 3}) can be encoded in the bit stream and, to decode the bit stream, the index map 2216 can be referenced. Each index 2320 in the index map can include a first set of bits 2322 (e.g., [2:0]) that indicates the index of the unique absolute value associated with each value to be encoded, with an additional bit 2324 (e.g., [3]) that indicates a sign associated with the value to be encoded. The header 2212 contains the number of UAVs encoded to enable the decoder to determine the number of unique absolute values 2214 to read before index map 2216 begins. As illustrated, the index map 2216 indicates that the first three values of the exemplary bit stream 2310 are stored in position three, one, and zero, respectively and each have positive signs.

In one embodiment, the set of unique absolute values 2214 are stored in sequential order, with the zeroth entry reserved for the value zero. In such configuration, the output buffer for the bit stream can be initialized to zero and during decode the least significant bit of each index 2320 can be checked to determine if a non-zero value is referenced before a full decode operation is performed for the index. If the least significant bit of the index is zero, indicating that the output value will be zero, decode for the indexed value can be bypassed.

FIG. 24 illustrates decode of an exemplary bit stream 2410 from a bit stream having the second layout 2220 of FIG. 22. In one embodiment, the illustrated layout of FIG. 24 is used for SM encoding mode. In one embodiment, SM encoding mode data is presented beginning with the third byte after a two-byte header, where the presence of EM encoding data is indicated by the first bit (e.g., [0]) of the second byte 2004 of the header shown in FIG. 20. The number of non-zero values in a sample block is indicated in the header 2222, along with metadata that indicates SM encoding is enabled. Next, a significance map 2224 is encoded that indicates a map of the non-zero values within the block, with a one bit entry associated with each value. The non-zero values 2226 of the sample are then encoded in order of appearance within the stream. To decode the exemplary bit stream 2410, decoder logic can initialize an output data buffer to zero. The decoder logic can then reference the significance map 2224 to determine which value in the bit stream are non-zero and unpack the values in order at the output locations indicated by the significance map. For example, a value of zero (0b0) in the significance map 2224 indicates that the corresponding decoded value is zero. A value of one (0b1) in the significance map 2224 indicates that the corresponding decoded value corresponds to the next successive entry in the non-zero values 2226 in the encoded bit stream.

FIG. 25 illustrates decode of an exemplary bit stream 2510 from an encoded bit stream having the third layout 2230 of FIG. 22. In one embodiment, the illustrated layout of FIG. 25 is used for UVC encoding mode. The number of non-zero values in a sample block is indicated in the header 2232, along with metadata that indicates UVC encoding mode is enabled. Next encoded are coordinates 2234 of the values indicating a location or offset of the non-zero values within the block. The non-zero values 2236 of the sample are then encoded in order of appearance within the stream. To decode the exemplary bit stream 2410, decoder logic can initialize an output data buffer to zero. As only a small number of non-zero values are found within the bit stream, the decoder logic can reference the coordinates 2234 of the values to determine specifically to place the non-zero values within the decoded stream.

FIG. 26 illustrates decode of an exemplary bit stream 2610 from a bit stream having the fourth layout 2240 of FIG. 22. In one embodiment, the illustrated layout of FIG. 26 is used for mean encoding mode. The number of encoded values can be indicated in the header 2242 along with metadata that indicates mean encoding is enabled. A mean value 2244 is stored after the header that is the arithmetic mean of the values of the bit stream, followed by the residual values 2246, which are the differences between the original values and the mean value 2244. In one embodiment, each residual value 2620 can be stored in a reduced bit representation that enables a first set of bits 2622 (e.g., [2:0]) to store the absolute value of the difference between the original value and the mean value 2244, with an additional bit 2624 (e.g., [3]) indicating the sign of the difference. For example, for an exemplary bit stream 2610 having a mean value 2244 of sixteen (e.g., 0x10), the values (16, 16, 14, 16, 18) can be represented residual values (0, 0, −2, 0, +2).

FIG. 27 is a flow diagram illustrating a method of performing CNN operations, according to an embodiment. In one embodiment the method of FIG. 27 is performed via the compute architecture 1900 of FIG. 19, although differing compute architectures can be configured to perform the illustrated method. The method includes decoding encoded kernel data while reading the encoded kernel data from memory, as shown at 2702. Decode can be performed by decode logic within compute logic, within a DMA controller associated with the compute block, or within a separate codec. In one embodiment, the kernel data can be persistently stored in non-volatile memory in the encoded format, loaded into memory in the encoded format, and remain in the encoded format until the kernel is consumed by the compute block.

Next, compute logic (e.g., the compute block, GPGPU logic, etc.) can be configured to generate feature map data for a CNN layer based on the kernel data, as shown at 2704. The feature map data for the CNN layer is then encoded during a write to memory, as shown at 2706. Computational logic can then read the encoded feature map data from memory and decode the encoded feature map data during the read, as shown at 2708. The computational logic can then process the feature map data as input feature map data for the next CNN layer, as shown at 2710.

FIG. 28 is a flow diagram of a method of encoding CNN data, according to an embodiment. In one embodiment, the method of FIG. 28 is performed via an encoder within a DMA controller, such as the encoded 1916 and DMA controller 1906 of FIG. 19. In one embodiment, the method can be performed via a separate codec that includes an encoder or can be performed by encode logic within a compute block.

As different encode methods have differing efficiency depending on the type of data to be encoded, encode logic can analyze the kernel or feature map data to encode, as shown at 2802. The encode logic can then determine an encode mode based on the data characteristics of the kernel or feature map data, as shown at 2804.

The encoding technique for a segment of kernel or feature map data can be determined based on data characteristics such as sparseness or dynamic range of the data. Data with a very low dynamic range can be encoded using mean encoding. Data having a very small number of unique values can be encoded using unique values coordinate encoding. In one embodiment UAV table encoding can be used as a default encoding method, with significance map (SM) encoding or table encoding (TE) enabled to further increase the efficiency of the UAV table encoding method for certain types of data.

The encoded can then encode the data based on the determined encode mode at 2806 and write metadata for the encoded data segment to enable decode of the encoded data segment, as shown at 2808. To decode an encoded data segment, a decoder can read the metadata to determine the amount of data encoded and the type of encoding used on the data. The decoder can then decode the indicated amount of data using a decode method associated with the indicated encode mode.

Compression Formats with Separate Metadata Storage

In some embodiments, the compression formats described herein can be modified to store certain elements of compression metadata separately from the compressed data. For example, and in one embodiment, the metadata can be stored within a separate control surface for a block of compressed data, where the metadata can indicate various compression details, including the type of compression used for the block of compressed data, and other descriptive information regarding the compressed data.

FIG. 29 illustrates compression using the significance map mode of compression, according to an additional embodiment. In one embodiment, a block of uncompressed data 2920 can be compressed using the significance map (SM) mode of compression, where a significance map 2224 is stored for the compressed data to indicate which portions of the data contain zero values and which portions of the data contain non-zero values. The significance map 2224 can indicate non-zero values with a bit value of 1 (0b1) and zero values with a bit value of zero (0b0). The non-zero values 2226 of the sample are then encoded in order of appearance within the stream.

In one embodiment, a separate block of metadata 2930 is stored for the region of compressed data to indicate the compression format for the portion, as well as other descriptive information that is relevant to the portion of compressed data.

FIG. 30 illustrates compression using the unique value coordinate mode of compression, according to an additional embodiment. In one embodiment, a block of uncompressed data 3020 can be can be compressed using the unique value coordinate (UVC) mode of compression, in which only unique values within a given block are stored. The set of unique non-zero values 2236 in the bit stream can be encoded in the order of appearance within the bit stream. In the illustrated variant of the UVC mode, the coordinates for each of the unique non-zero values 2236 within the bit stream can be stored within the metadata 3030, instead in coordinates (e.g., coordinates 2234 as in FIG. 25) stored adjacent to the unique non-zero values within the compressed bit stream.

FIG. 31 illustrates encoding and decoding operations with separate metadata, according to an embodiment. In one embodiment, raw data 3102 can be encoded via an encoder 3104 and decoded with a decoder 3124. In one embodiment, the encoder 3104 and decoder 3124 can each be included in a DMA controller 3103. Encoded data 3112 can occupy a reduced memory footprint in memory relative to un-encoded data, freeing memory to be used to store additional data. In one embodiment, the encoded data 3112 and metadata 3114 can be stored separately in memory 3110, allowing the metadata 3114 to be readily transmitted in a separate data stream than the encoded data 3112. Enabling the metadata 3114 to be transmitted as a separate data stream enables the metadata 3114 to be cached and/or pre-fetched by the DMA controller 3103, which can pre-configure the decoder 3124 based on the pre-fetched metadata 3114. The configured decoder 3124 can then output decoded data 3122 for processing by computational elements described herein.

FIG. 32 illustrates an exemplary codec system 3200, according to an embodiment. The codec system 3200 can be included in a general-purpose graphics processor, general-purpose processor, or a many-integrated-core processor as described herein to compress data associated with a neural network before such data is stored to memory, reducing the memory footprint required to perform training and/or inferencing operations using the neural network.

In one embodiment, the codec system 3200 includes a DMA controller 3210 that is communicatively coupled with a host 3230. The host 3230 can be connected to the DMA controller 3210 via a fabric interconnect, memory interconnect, or peripheral interconnect. The DMA controller 3210 includes a controller 3215, an encoder 3219 and a decoder 3221. The host 3230 can communicate with configuration registers 3211 via an advanced peripheral bus (ABP) 3212. The configuration registers 3211 can be used to configure operations of the controller 3215, encoder 3219 and decoder 3221. When the controller 3215 is configured to perform DMA operations to and from memory, the controller 3215 can be configured to encode data written to memory via the encoder and to decode data read from memory via the decoder 3221.

The encode/decode unit 3210 includes a primary interface 3226 and a secondary interface 3224, which enables the encode/decode unit 3210 to communicate with an interconnect fabric or interconnect bus. A variety of interconnects can be coupled to the primary interface 3226 and the secondary interface 3224 in various embodiments. In one embodiment, the primary interface 3226 and interface slave 3226 can connect to an advanced extensible interface used in high clock frequency systems. The primary interface 3226 and secondary interface 3224 each include connection channels including but not limited to a read/write channel 3234A-3234B and an address read/address write channel 3236A-3236B. A metadata buffer 3214 is coupled between the primary interface 3226 and the controller 3215 to buffer a metadata stream read from memory of the host 3230. In one embodiment, a bus access management channel (bchan) 3213 couples the primary interface 3226 with the controller 3215.

In one embodiment, an address read/address write channel 3216 is provided by the controller 3215 for output via the secondary interface 3224. Data to be written to memory is provided via a write channel 3217, which can be temporarily stored in one or more buffers 3218 before being encoded by the encoder 3219 and written to memory of the host 3230. Data to be read can be stored in a reorder buffer 3220 before being decoded by the decoder 3221 and written out via a read channel 3222. The reorder buffer 3221 enables the DMA controller 3210 to complete read operations out-of-order or receipt.

Compression for Deep Learning in Case of Sparse Values Mapped to Non-Zero Value

The encoding and decoding techniques and logic described herein enable data associated with a neural network (e.g., weight data, activation data, other parameter data etc.) to be efficiently compressed and decompressed during writes and reads to memory, reducing the memory footprint required for computational operations using neural networks. One embodiment enhances the above described logic and techniques with an additional encode mode to provide more efficient encoding for techniques that attempt to compress a data stream by mapping a value having a high occurrence frequency with a zero value. In such embodiment, the highest frequency value in a bit stream is identified and a significance map is generated in which the positions of the highest frequency value is mapped to zero (0b0) in the significance map. Other values are indicated by a one (0b1) in the significance map. A bit stream of remaining values can be sent in an encoded bit stream. A separate stream of metadata is also transmitted. In this encoding format, the first byte of the encoded bit stream contains the high frequency value. Instead of replacing zeros, as in significance map mode, the high frequency value will be replaced.

FIG. 33 illustrates compression for machine learning data having a single high frequency value, according to embodiments described herein. In one embodiment, an uncompressed data block 3310 can be processed by encode logic described herein. The processing can include an operation 3315 to generate a histogram of the frequency of occurrence for each element in the uncompressed data block 3310. A histogram 3320 can be generated of the frequency of occurrence of each element in the uncompressed data block 3310 to determine the element having the highest frequency 3321. The value of the highest frequency element 3224 can be stored in the encode stream, followed by a significance map 2224 for the block of data that indicates an original position of each element within the uncompressed data block 3310. The non-zero values 2226 of the sample are then encoded in order of appearance within the stream, and metadata 3330 is generated for the block of compressed data to describe the type of compression used for the block of data. Encoded data can then be written to memory along with the metadata 3330 that describes the compression for the encoded data.

In one embodiment, the illustrated variant of significance map encoding in which zero is mapped to the high frequency value can be specified as a distinct encoding type relative to significance map encoding, and can have a different encoding identifier within the metadata for the encode block. For example, and in one embodiment, this separate encoding technique can be associated with a specific encoding technique used by one or more neural network or machine learning frameworks, such as but not limited to the low precision general matrix multiplication (GEMMlowp) library, or an equivalent library in which sparse values are mapped to high frequency values.

FIG. 34 is a flow diagram illustrating a process 3400 to enable compression for deep learning where sparse values mapped to non-zero value, according to an embodiment. The process can be implemented using hardware logic described herein, such as the codec system 3200 of FIG. 32. Such codec system can be integrated into a processing device (e.g., CPU, MIC processor, GPGPU, FPGA, etc.) as described herein. In one embodiment, the codec system is integrated into a DMA controller.

As shown at block 3402, logic associated with or integrated within a codec system can analyze kernel data to be encoded. Such logic can determine that a high-frequency value of the kernel data is mapped to a zero value, as shown at block 3404. The codec can then map the high-frequency value to zero and store a single instance of the high-frequency value in the encoded data, as shown at block 3406. As shown at block 3408, the coded can then encode the kernel data into the encoded kernel data, the encoded kernel data including the map of the high-frequency value. In one embodiment, the codec can encode the kernel data using an encoding map in which zero is mapped to the high frequency value, as shown in FIG. 33. At block 3410, the codec can then write the encoded kernel data and associated metadata to memory. The metadata can be used to reverse the encoding when the kernel data is read from memory, and includes an identifier for the encoding technique used to encode a given block of data.

FIG. 35 is a block diagram of a computing device 3500 including a graphics processor 3504, according to an embodiment. The computing device 3500 can be a computing device such as the data processing system 100 as in of FIG. 1. The computing device 3500 may also be or be included within a communication device such as a set-top box (e.g., Internet-based cable television set-top boxes, etc.), global positioning system (GPS)-based devices, etc. The computing device 3500 may also be or be included within mobile computing devices such as cellular phones, smartphones, personal digital assistants (PDAs), tablet computers, laptop computers, e-readers, smart televisions, television platforms, wearable devices (e.g., glasses, watches, bracelets, smartcards, jewelry, clothing items, etc.), media players, etc. For example, in one embodiment, the computing device 3500 includes a mobile computing device employing an integrated circuit (“IC”), such as system on a chip (“SoC” or “SOC”), integrating various hardware and/or software components of computing device 3500 on a single chip.

The computing device 3500 includes a graphics processor 3504. The graphics processor 3504 represents any graphics processor described herein. The graphics processor includes one or more graphics engine(s), graphics processor cores, and other graphics execution resources as described herein. Such graphics execution resources can be presented in the forms including but not limited to execution units, shader engines, fragment processors, vertex processors, streaming multiprocessors, graphics processor clusters, or any collection of computing resources suitable for the processing of graphics resources or image resources, or performing general purpose computational operations in a heterogeneous processor.

In one embodiment, the graphics processor 3504 includes a cache 3514, which can be a single cache or divided into multiple segments of cache memory, including but not limited to any number of L1, L2, L3, or L4 caches, render caches, depth caches, sampler caches, and/or shader unit caches. In some embodiments, the graphics processor 3504 includes a CNN logic unit 3524, a GPGPU engine 3544, and a DMA unit 3554. In one embodiment, the CNN logic unit 3524 is a fixed function logic unit that can be configured to generate feature map data using the GPGPU engine 3544. In one embodiment the DMA unit 3554 includes a codec 3534 containing encode and decode logic to encode neural network data before the data is transmitted to memory via the DMA unit 3554. In one embodiment, the codec 3534 can be configured to losslessly encode and decode feature map data and kernel data for a neural network. In an alternate embodiment, encode logic of the feature map codec is located in the DMA unit 3554, with decode logic being locating in the CNN logic unit 3524 and/or the GPGPU engine 3544.

As illustrated, in one embodiment, and in addition to the graphics processor 3504, the computing device 3500 may further include any number and type of hardware components and/or software components, including, but not limited to an application processor 3506, memory 3508, and input/output (I/O) sources 3510. The application processor 3506 can interact with a hardware graphics pipeline, as illustrated with reference to FIG. 3, to share graphics pipeline functionality. Processed data is stored in a buffer in the hardware graphics pipeline and state information is stored in memory 3508. The resulting data can be transferred to a display controller for output via a display device, such as the display device 320 of FIG. 3. The display device may be of various types, such as Cathode Ray Tube (CRT), Thin Film Transistor (TFT), Liquid Crystal Display (LCD), Organic Light Emitting Diode (OLED) array, etc., and may be configured to display information to a user via a graphical user interface.

The application processor 3506 can include one or processors, such as processor(s) 102 of FIG. 1, and may be the central processing unit (CPU) that is used at least in part to execute an operating system (OS) 3502 for the computing device 3500. The OS 3502 can serve as an interface between hardware and/or physical resources of the computing device 3500 and one or more users. The OS 3502 can include driver logic 3522 for various hardware devices in the computing device 3500. The driver logic 3522 can include graphics driver logic 3523 such as the user mode graphics driver 1026 and/or kernel mode graphics driver 1029 of FIG. 10.

It is contemplated that in some embodiments the graphics processor 3504 may exist as part of the application processor 3506 (such as part of a physical CPU package) in which case, at least a portion of the memory 3508 may be shared by the application processor 3506 and graphics processor 3504, although at least a portion of the memory 3508 may be exclusive to the graphics processor 3504, or the graphics processor 3504 may have a separate store of memory. The memory 3508 may comprise a pre-allocated region of a buffer (e.g., framebuffer); however, it should be understood by one of ordinary skill in the art that the embodiments are not so limited, and that any memory accessible to the lower graphics pipeline may be used. The memory 3508 may include various forms of random access memory (RAM) (e.g., SDRAM, SRAM, etc.) comprising an application that makes use of the graphics processor 3504 to render a desktop or 3D graphics scene. A memory controller hub, such as memory controller 116 of FIG. 1, may access data in the memory 3508 and forward it to graphics processor 3504 for graphics pipeline processing. The memory 3508 may be made available to other components within the computing device 3500. For example, any data (e.g., input graphics data) received from various I/O sources 3510 of the computing device 3500 can be temporarily queued into memory 3508 prior to their being operated upon by one or more processor(s) (e.g., application processor 3506) in the implementation of a software program or application. Similarly, data that a software program determines should be sent from the computing device 3500 to an outside entity through one of the computing system interfaces, or stored into an internal storage element, is often temporarily queued in memory 3508 prior to its being transmitted or stored.

The I/O sources can include devices such as touchscreens, touch panels, touch pads, virtual or regular keyboards, virtual or regular mice, ports, connectors, network devices, or the like, and can attach via a platform controller hub 130 as referenced in FIG. 1. Additionally, the I/O sources 3510 may include one or more I/O devices that are implemented for transferring data to and/or from the computing device 3500 (e.g., a networking adapter); or, for a large-scale non-volatile storage within the computing device 3500 (e.g., hard disk drive). User input devices, including alphanumeric and other keys, may be used to communicate information and command selections to graphics processor 3504. Another type of user input device is cursor control, such as a mouse, a trackball, a touchscreen, a touchpad, or cursor direction keys to communicate direction information and command selections to GPU and to control cursor movement on the display device. Camera and microphone arrays of the computing device 3500 may be employed to observe gestures, record audio and video and to receive and transmit visual and audio commands.

I/O sources 3510 configured as network interfaces can provide access to a network, such as a LAN, a wide area network (WAN), a metropolitan area network (MAN), a personal area network (PAN), Bluetooth, a cloud network, a cellular or mobile network (e.g., 3^(rd) Generation (3G), 4^(th) Generation (4G), etc.), an intranet, the Internet, etc. Network interface(s) may include, for example, a wireless network interface having one or more antenna(e). Network interface(s) may also include, for example, a wired network interface to communicate with remote devices via network cable, which may be, for example, an Ethernet cable, a coaxial cable, a fiber optic cable, a serial cable, or a parallel cable.

Network interface(s) may provide access to a LAN, for example, by conforming to IEEE 802.11 standards, and/or the wireless network interface may provide access to a personal area network, for example, by conforming to Bluetooth standards. Other wireless network interfaces and/or protocols, including previous and subsequent versions of the standards, may also be supported. In addition to, or instead of, communication via the wireless LAN standards, network interface(s) may provide wireless communication using, for example, Time Division, Multiple Access (TDMA) protocols, Global Systems for Mobile Communications (GSM) protocols, Code Division, Multiple Access (CDMA) protocols, and/or any other type of wireless communications protocols.

It is to be appreciated that a lesser or more equipped system than the example described above may be preferred for certain implementations. Therefore, the configuration of the computing device 3500 may vary from implementation to implementation depending upon numerous factors, such as price constraints, performance requirements, technological improvements, or other circumstances. Examples include (without limitation) a mobile device, a personal digital assistant, a mobile computing device, a smartphone, a cellular telephone, a handset, a one-way pager, a two-way pager, a messaging device, a computer, a personal computer (PC), a desktop computer, a laptop computer, a notebook computer, a handheld computer, a tablet computer, a server, a server array or server farm, a web server, a network server, an Internet server, a work station, a mini-computer, a main frame computer, a supercomputer, a network appliance, a web appliance, a distributed computing system, multiprocessor systems, processor-based systems, consumer electronics, programmable consumer electronics, television, digital television, set top box, wireless access point, base station, subscriber station, mobile subscriber center, radio network controller, router, hub, gateway, bridge, switch, machine, or combinations thereof.

Embodiments may be implemented as any one, or a combination of: one or more microchips or integrated circuits interconnected using a parent-board, hardwired logic, software stored by a memory device and executed by a microprocessor, firmware, an application specific integrated circuit (ASIC), and/or a field programmable gate array (FPGA). The term “logic” may include, by way of example, software or hardware and/or combinations of software and hardware.

Embodiments may be provided, for example, as a computer program product which may include one or more machine-readable media having stored thereon machine-executable instructions that, when executed by one or more machines such as a computer, network of computers, or other electronic devices, may result in the one or more machines carrying out operations in accordance with embodiments described herein. A machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (Compact Disc-Read Only Memories), and magneto-optical disks, ROMs, RAMs, EPROMs (Erasable Programmable Read Only Memories), EEPROMs (Electrically Erasable Programmable Read Only Memories), magnetic or optical cards, flash memory, or other type of non-transitory machine-readable media suitable for storing machine-executable instructions.

Moreover, embodiments may be downloaded as a computer program product, wherein the program may be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of one or more data signals embodied in and/or modulated by a carrier wave or other propagation medium via a communication link (e.g., a modem and/or network connection).

The following clauses and/or examples pertain to specific embodiments or examples thereof. Specifics in the examples may be used anywhere in one or more embodiments. The various features of the different embodiments or examples may be variously combined with some features included and others excluded to suit a variety of different applications. Examples may include subject matter such as a method, means for performing acts of the method, at least one machine-readable medium including instructions that, when performed by a machine cause the machine to perform acts of the method, or of an apparatus or system according to embodiments and examples described herein. Various components can be a means for performing the operations or functions described.

Embodiments described herein provide a processing apparatus comprising compute logic to generate neural network data for a convolutional neural network (CNN) and write the neural network data to a memory buffer. The compute logic additionally includes a direct memory access (DMA) controller including a hardware codec having an encode unit and a decode unit, the DMA controller to read the neural network data from the memory buffer, encode the neural network data via the encode unit, write encoded neural network data to a memory device coupled with the processing apparatus, write metadata for the encoded neural network data to the memory device coupled with the processing apparatus, and decode encoded neural network data via the decode unit in response to a request from the compute logic. In one embodiment, the compute logic can request the DMA controller to read the encoded feature map data from the memory device. In response to the request from the compute logic the DMA controller can pre-fetch the metadata for the encoded feature map data and decode the encoded neural network data based on the pre-fetched metadata. The neural network data can include feature map data and kernel data.

In one embodiment, the hardware codec can encode the feature map data using an encode mode selected from a set of multiple encode modes. The set of multiple encode modes include an encode mode to the neural network data in a reduced-bit representation via encode of two or more of unique absolute values, non-zero values, and residual values. The set of multiple encode modes can additionally include an encode mode to encode an arithmetic sequence of values in a reduced bit representation. In one embodiment, the set of multiple encode modes additionally includes an encode mode to encode a neural network data having a high frequency value in a reduced bit representation. The compute logic, is included within a general-purpose graphics processing unit.

One embodiment provides for a method of performing processing operations to enable a convolutional neural network (CNN) the method comprising decoding encoded kernel data for the CNN while reading the encoded kernel data from memory; generating feature map data for a layer of the CNN via compute logic within a general-purpose graphics processing unit using decoded kernel data; encoding the feature map data for the layer of the CNN via hardware encode logic within a direct memory access (DMA) controller during a write to memory; decoding encoded feature map data while reading the encoded feature map data from memory; and processing the feature map data as input feature map data for a next layer of the CNN, wherein decoding the encoded kernel data includes pre-fetching metadata associated with the encoded kernel data, the metadata associated with the encoded kernel data stored separately, in a different set of memory addresses, from the encoded kernel data.

In one embodiment, decoding the encoded feature map data includes pre-fetching metadata associated with the encoded feature map data, the metadata associated with the encoded feature map data stored separately from the encoded feature map data. The feature map data can be decoded via hardware decode logic within the DMA controller. In one embodiment, encoding the feature map data via hardware encode logic is performed using one or more encode modes selected from a set of multiple encode modes. The set of multiple encode modes includes include encode modes to store kernel data or feature map data in a reduced-bit representation via encode of two or more of unique absolute values, non-zero values, and residual values and wherein the set of multiple encode modes additionally include an encode mode to encode an arithmetic sequence of values in a reduced-bit representation.

In one embodiment, the method additionally includes encoding kernel data for the CNN via the hardware encode logic within the DMA controller. Encoding the kernel data can include analyzing the kernel data to encode; determining that a high-frequency value of the kernel data is mapped to a zero value; storing the high-frequency value to a block of encoded kernel data; encoding the kernel data into the block of encoded kernel data, the encoded kernel data including the map of the high-frequency value; and writing encoded kernel data and the metadata associated with the encoded kernel data to memory.

One embodiment provides for a data processing system configured to perform operations to enable a convolutional neural network (CNN). The data processing system comprises a memory device to store feature map data for the CNN; a non-volatile storage device to persistently store kernel data for the CNN; a processor including a general-purpose graphics processor compute block and a DMA controller. The general-purpose graphics processor compute block can generate output feature map data for the CNN using the kernel data and write the output feature map data to a memory buffer within the processor. The DMA controller includes a hardware codec including an encode unit to automatically encode the output feature map data during a write of the output feature map data to the memory device. In one embodiment, the data processing system can include any aspect of the processing apparatus described above and can perform operations of a method as described herein.

Those skilled in the art will appreciate from the foregoing description that the broad techniques of the embodiments can be implemented in a variety of forms. Therefore, while the embodiments have been described in connection with particular examples thereof, the true scope of the embodiments should not be so limited since other modifications will become apparent to the skilled practitioner upon a study of the drawings, specification, and following claims. 

What is claimed is:
 1. A graphics processor comprising: compute circuitry to generate neural network data for a convolutional neural network (CNN) and write the neural network data to a memory buffer; and a direct memory access (DMA) controller including a hardware codec having an encode unit and a decode unit, the DMA controller to read the neural network data from the memory buffer, encode the neural network data via the encode unit, write encoded neural network data to a memory device coupled with the graphics processor, write metadata for the encoded neural network data to the memory device coupled with the graphics processor, the metadata written to a different block of memory in the memory device than the encoded neural network data, and automatically decode encoded neural network data via the decode unit in response to a request from the compute circuitry to read the neural network data.
 2. The graphics processor as in claim 1, wherein: the compute circuitry to request the DMA controller to read the encoded neural network data from the memory device; in response to the request from the compute circuitry the DMA controller to pre-fetch the metadata for the encoded neural network data; and the DMA controller to decode the encoded neural network data based on the pre-fetched metadata.
 3. The graphics processor as in claim 1, wherein the neural network data includes feature map data and kernel data.
 4. The graphics processor as in claim 3, wherein the hardware codec is to encode the feature map data using an encode mode selected from a set of multiple encode modes.
 5. The graphics processor as in claim 4, wherein the set of multiple encode modes include an encode mode to the neural network data in a reduced-bit representation via encode of two or more of unique absolute values, non-zero values, and residual values.
 6. The graphics processor as in claim 5, wherein the set of multiple encode modes additionally include an encode mode to encode an arithmetic sequence of values in a reduced bit representation.
 7. The graphics processor as in claim 6, wherein the set of multiple encode modes additionally include an encode mode to encode a neural network data having a high frequency value in a reduced bit representation.
 8. The graphics processor as in claim 1, wherein the DMA controller is to automatically decode encoded neural network data via the decode unit based in part on a map of a high-frequency value of the neural network data to a zero value, wherein the high-frequency value has a higher frequency of occurrence relative to other values in the neural network data.
 9. A method comprising: decoding encoded kernel data for a convolutional neural network (CNN) while reading the encoded kernel data from memory, the kernel data encoded via a hardware encoder within a direct memory access (DMA) controller during a write operation, wherein encoding the kernel data for the CNN during the write operation includes: analyzing the kernel data to encode; determining that a high-frequency value of the kernel data is mapped to a zero value, wherein the high-frequency value has a higher frequency of occurrence relative to other values in the kernel data; storing the high-frequency value to a block of encoded kernel data; encoding the kernel data into the block of encoded kernel data, the encoded kernel data including the map of the high-frequency value; and performing an operation to write encoded kernel data and metadata associated with the encoded kernel data to memory; and generating feature map data for a layer of the CNN via compute logic within a general-purpose graphics processing unit using decoded kernel data.
 10. The method as in claim 9, additionally comprising: encoding the feature map data for the layer of the CNN via hardware encoder within the DMA controller during a write to memory; decoding encoded feature map data while reading the encoded feature map data from memory; and processing the feature map data as input feature map data for a next layer of the CNN, wherein decoding the encoded kernel data includes pre-fetching metadata associated with the encoded kernel data, the metadata associated with the encoded kernel data stored separately from the encoded kernel data.
 11. The method as in claim 10, wherein decoding the encoded feature map data includes pre-fetching metadata associated with the encoded feature map data, the metadata associated with the encoded feature map data stored separately from the encoded feature map data.
 12. The method as in claim 10, additionally comprising decoding the feature map data via hardware decode logic within the DMA controller.
 13. The method as in claim 10, additionally comprising encoding the feature map data via hardware encoder using one or more encode modes selected from a set of multiple encode modes, wherein the set of multiple encode modes includes include encode modes to store kernel data or feature map data in a reduced-bit representation via encode of two or more of unique absolute values, non-zero values, and residual values and wherein the set of multiple encode modes additionally include an encode mode to encode an arithmetic sequence of values in a reduced-bit representation.
 14. The method as in claim 10, wherein encoding the kernel data for the CNN includes writing the metadata to a different block of memory than the encoded kernel data.
 15. A data processing system comprising: a memory device to store feature map data for a convolutional neural network (CNN); a non-volatile storage device to persistently store kernel data for the CNN; and a processor including a general-purpose graphics processor compute block and a direct memory access (DMA) controller, the DMA controller including a hardware codec including an encode unit to automatically encode kernel data during a write operation to the memory device, wherein to automatically encode the kernel data during the write operation, the DMA controller is to: analyze the kernel data to encode, determine that a high-frequency value of the kernel data is mapped to a zero value, wherein the high-frequency value has a higher frequency of occurrence relative to other values in the kernel data; generate a map of each of a plurality of instances of the high-frequency value in the kernel data; encode the kernel data into encoded kernel data, the encoded kernel data including the map of the plurality of instances of the high frequency value; and write the encoded kernel data and metadata associated with the encoded kernel data to memory.
 16. The data processing system as in claim 15, wherein the DMA controller includes a hardware codec including a decode unit to automatically decode output feature map to be read from the memory device.
 17. The data processing system as in claim 16, the kernel data for the CNN stored on the non-volatile storage device as encoded kernel data, the encoded kernel data to be loaded to the memory device, and the DMA controller to automatically decode the encoded kernel data during a read of the encoded kernel data from the memory device.
 18. The data processing system as in claim 17, wherein the general-purpose graphics processor compute block is to generate output feature map data for the CNN using the kernel data and write the output feature map data to a memory buffer within the processor.
 19. The data processing system as in claim 18, wherein the DMA controller, via the encode unit of the hardware codec, is to write the feature map data to the memory device in an encoded format including one or more encode modes selected from a set of multiple encode modes, the set of multiple encode modes including encode modes based on two or more of unique absolute value encoding, arithmetic sequence encoding, significance map encoding, unique value coordinate encoding, and mean encoding value encoding.
 20. The data processing system as in claim 18, wherein to automatically encode the kernel data, the DMA controller is to write the metadata to a different block of memory than the encoded kernel data.
 21. An apparatus comprising: a memory device to store feature map data for a convolutional neural network (CNN); a non-volatile storage device to persistently store kernel data for the CNN; a processor including a general-purpose graphics processor compute block and a direct memory access (DMA) controller, the DMA controller to automatically encode kernel data during a write operation to the memory device; wherein the general-purpose graphics processor compute block is to generate output feature map data for the CNN using the kernel data and write the output feature map data to a memory buffer within the processor; wherein the DMA controller includes a hardware codec including an encode unit to automatically encode the output feature map data during a write operation performed for the output feature map data to the memory device; and wherein to automatically encode the kernel data during the write operation, the DMA controller is to analyze the kernel data to encode, determine that a high-frequency value of the kernel data is mapped to a zero value, generate a map of each of a plurality of instances of the high-frequency value in the kernel data, encode the kernel data into encoded kernel data, the encoded kernel data including the map of each of the plurality of instances of the high frequency value, and write the encoded kernel data and metadata associated with the encoded kernel data to the memory device, the metadata written to a different block of memory in the memory device than the encoded kernel data. 